pwc-archive/papers-with-abstracts huggingface dataset:
FDSOI Process Based MIV-transistor Utilization for Standard Cell Designs in Monolithic 3D Integration
Authors: Madhava Sarma Vemuri, Umamaheswara Rao Tida
Date: 2023-06-24 00:00:00

Monolithic Three-Dimensional Integrated Circuits (M3D-IC) has become an attractive option to increase the transistor density. In M3D-IC, substrate layers are realized on top of previous layers using sequential integration techniques. Recent works in M3D-IC have demonstrated the feasibility of FDSOI process-based M3D-IC implementations and, Metal inter-layer vias (MIVs) are used to provide connections between the inter-layer devices. Since MIVs are extended from bottom layer to top layer, they occupy a small area resulting in area overhead. Additionally, a minimum separation is required to facilitate connection between MIV and transistors which increases this overhead further. Towards this, we studied the alternate utilization of MIV to create MIV-transistors with varying channels. We have also presented a strategy to extract the Spice parameters of the proposed models using level 70 spice parameters. Finally, a standard cell based gate level comparison is presented to compare the Power, Performance and Area (PPA) metrics of the traditional two layer 2D FDSOI transistor implementation with the proposed models. Simulation results from standard cell designs suggest that the proposed methodology can reduce 18\% layout area on average compared to the traditional approach. In addition, power consumption and delay time of the standard cells are reduced by 1\% and 3\% on average respectively.
Multi-agent Deep Reinforcement Learning for Distributed Load Restoration
Authors: Linh Vu, Tuyen Vu, Thanh-Long Vu, Anurag Srivastava
Date: 2023-06-24 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

This paper addresses the load restoration problem after power outage events. Our primary proposed methodology is using multi-agent deep reinforcement learning to optimize the load restoration process in distribution systems, modeled as networked microgrids, via determining the optimal operational sequence of circuit breakers (switches). An innovative invalid action masking technique is incorporated into the multi-agent method to handle both the physical constraints in the restoration process and the curse of dimensionality as the action space of operational decisions grows exponentially with the number of circuit breakers. The features of our proposed method include centralized training for multi-agents to overcome non-stationary environment problems, decentralized execution to ease the deployment, and zero constraint violations to prevent harmful actions. Our simulations are performed in OpenDSS and Python environments to demonstrate the effectiveness of the proposed approach using the IEEE 13, 123, and 8500-node distribution test feeders. The results show that the proposed algorithm can achieve a significantly better learning curve and stability than the conventional methods.
A Cyber-HIL for Investigating Control Systems in Ship Cyber Physical Systems under Communication Issues and Cyber Attacks
Authors: Linh Vu, Lam Nguyen, Mahmoud Abdelaal, Tuyen Vu, Osama Mohammed
Date: 2023-06-24 00:00:00

This paper presents a novel Cyber-Hardware-in-the-Loop (Cyber-HIL) platform for assessing control operation in ship cyber-physical systems. The proposed platform employs cutting-edge technologies, including Docker containers, real-time simulator $OPAL-RT$, and network emulator $ns3$, to create a secure and controlled testing and deployment environment for investigating the potential impact of cyber attack threats on ship control systems. Real-time experiments were conducted using an advanced load-shedding controller as a control object in both synchronous and asynchronous manners, showcasing the platform's versatility and effectiveness in identifying vulnerabilities and improving overall Ship Cyber Physical System (SCPS) security. Furthermore, the performance of the load-shedding controller under cyber attacks was evaluated by conducting tests with man-in-the-middle (MITM) and denial-of-service (DoS) attacks. These attacks were implemented on the communication channels between the controller and the simulated ship system, emulating real-world scenarios. The proposed Cyber-HIL platform provides a comprehensive and effective approach to test and validate the security of ship control systems in the face of cyber threats.
A Novel Handover Mechanism for Visible Light Communication Network
Authors: M. A. N. Perera, N. G. K. R. Wijewardhana, A. A. D. T Nissanka, S. A. H. A. Suraweera, G. M. R. I. Godaliyadda
Date: 2023-06-24 00:00:00

Visible light communication (VLC) is an emerging technology and considered as an alternative to overcome some of the disadvantages of radio frequency communication technology in an indoor environment. However, the line of sight nature of the technology limits the user mobility and create new challenges to provides seamless network coverage under user mobility scenarios. In VLC multi access points and multi cell based network, co channel interference (CCI) between neighbor cells limits the overall performance. Therefore, by following the already existing VLC handover systems, this statistical parameter based novel handover method is designed. This paper proposes a handover mechanism for indoor VLC systems by introducing cell ID bits and statistical kurtosis values of the cell ID waveforms as metric for the handover initiation. As advantage of the method, effects from the CCI can be eliminate when measuring signal strengths in the signal overlapping cell boundary area. Also the handover criteria are adoptive to the different ambient lighting conditions compared to existing pre-configured light intensity threshold based handover systems. With the use of bit error rate (BER), the experiment results showed that Kurtosis value of the cell ID waveforms can be used as metric to initiate network handover in indoor VLC systems.
Propulsion-Free Cross-Track Control of a LEO Small-Satellite Constellation with Differential Drag
Authors: Giusy Falcone, Jacob B. Willis, Zachary Manchester
Date: 2023-06-24 00:00:00

In this work, we achieve propellantless control of both cross-track and along-track separation of a satellite formation by manipulating atmospheric drag. Increasing the differential drag of one satellite with respect to another directly introduces along-track separation, while cross-track separation can be achieved by taking advantage of higher-order terms in the Earth's gravitational field that are functions of altitude. We present an algorithm for solving an n-satellite formation flying problem based on linear programming. We demonstrate this algorithm in a receeding-horizon control scheme in the presence of disturbances and modeling errors in a high-fidelity closed-loop orbital dynamics simulation. Our results show that separation distances of hundreds of kilometers can be achieved by a small-satellite formation in low-Earth orbit over a few months.
Characterization, Verification and Computation of Robust Controlled Invariants for Monotone Dynamical Systems
Authors: Adnane Saoud, Murat Arcak
Date: 2023-06-24 00:00:00

In this paper, we consider the problem of computing robust controlled invariants for discrete-time monotone dynamical systems. We consider different classes of monotone systems depending on whether the sets of states, control inputs and disturbances respect a given partial order. Then, we present set-based and trajectory-based characterizations of robust controlled invariants for the considered class of systems. Based on these characterizations, we propose algorithmic approaches for the verification and computation of robust controlled invariants. Finally, illustrative examples are provided showing the merits of the proposed approach.
Ultrafast Cardiac Imaging Using Deep Learning For Speckle-Tracking Echocardiography
Authors: Jingfeng Lu, Fabien Millioz, François Varray, Jonathan Porée, Jean Provost, Olivier Bernard, Damien Garcia, Denis Friboulet
Date: 2023-06-25 00:00:00
Tasks: Deep Learning, Image Reconstruction, Motion Compensation, Motion Estimation

High-quality ultrafast ultrasound imaging is based on coherent compounding from multiple transmissions of plane waves (PW) or diverging waves (DW). However, compounding results in reduced frame rate, as well as destructive interferences from high-velocity tissue motion if motion compensation (MoCo) is not considered. While many studies have recently shown the interest of deep learning for the reconstruction of high-quality static images from PW or DW, its ability to achieve such performance while maintaining the capability of tracking cardiac motion has yet to be assessed. In this paper, we addressed such issue by deploying a complex-weighted convolutional neural network (CNN) for image reconstruction and a state-of-the-art speckle tracking method. The evaluation of this approach was first performed by designing an adapted simulation framework, which provides specific reference data, i.e. high quality, motion artifact-free cardiac images. The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts. The performance was then further evaluated on non-simulated, experimental in vitro data, using a spinning disk phantom. This experiment demonstrated that our approach yielded high-quality image reconstruction and motion estimation, under a large range of velocities and outperforms a state-of-the-art MoCo-based approach at high velocities. Our method was finally assessed on in vivo datasets and showed consistent improvement in image quality and motion estimation compared to standard compounding. This demonstrates the feasibility and effectiveness of deep learning reconstruction for ultrafast speckle-tracking echocardiography.
Measuring unequal distribution of pandemic severity across census years, variants of concern and interventions
Authors: Quang Dang Nguyen, Sheryl L. Chang, Christina M. Jamerlan, Mikhail Prokopenko
Date: 2023-06-26 00:00:00

Diverse and complex intervention policies deployed over the last years have shown varied effectiveness in controlling the COVID-19 pandemic. However, a systematic analysis and modelling of the combined effects of different viral lineages and complex intervention policies remains a challenge. Using large-scale agent-based modelling and a high-resolution computational simulation matching census-based demographics of Australia, we carried out a systematic comparative analysis of several COVID-19 pandemic scenarios. The scenarios covered two most recent Australian census years (2016 and 2021), three variants of concern (ancestral, Delta and Omicron), and five representative intervention policies. In addition, we introduced pandemic Lorenz curves measuring an unequal distribution of the pandemic severity across local areas. We quantified nonlinear effects of population heterogeneity on the pandemic severity, highlighting that (i) the population growth amplifies pandemic peaks, (ii) the changes in population size amplify the peak incidence more than the changes in density, and (iii) the pandemic severity is distributed unequally across local areas. We also examined and delineated the effects of urbanisation on the incidence bimodality, distinguishing between urban and regional pandemic waves. Finally, we quantified and examined the impact of school closures, complemented by partial interventions, and identified the conditions when inclusion of school closures may decisively control the transmission. Our results suggest that (a) public health response to long-lasting pandemics must be frequently reviewed and adapted to demographic changes, (b) in order to control recurrent waves, mass-vaccination rollouts need to be complemented by partial NPIs, and (c) healthcare and vaccination resources need to be prioritised towards the localities and regions with high population growth and/or high density.
Causal potency of consciousness in the physical world
Authors: Danko D. Georgiev
Date: 2023-06-26 00:00:00

The evolution of the human mind through natural selection mandates that our conscious experiences are causally potent in order to leave a tangible impact upon the surrounding physical world. Any attempt to construct a functional theory of the conscious mind within the framework of classical physics, however, inevitably leads to causally impotent conscious experiences in direct contradiction to evolution theory. Here, we derive several rigorous theorems that identify the origin of the latter impasse in the mathematical properties of ordinary differential equations employed in combination with the alleged functional production of the mind by the brain. Then, we demonstrate that a mind--brain theory consistent with causally potent conscious experiences is provided by modern quantum physics, in which the unobservable conscious mind is reductively identified with the quantum state of the brain and the observable brain is constructed by the physical measurement of quantum brain observables. The resulting quantum stochastic dynamics obtained from sequential quantum measurements of the brain is governed by stochastic differential equations, which permit genuine free will exercised through sequential conscious choices of future courses of action. Thus, quantum reductionism provides a solid theoretical foundation for the causal potency of consciousness, free will and cultural transmission.
Marginal Effects for Probit and Tobit with Endogeneity
Authors: Kirill S. Evdokimov, Ilze Kalnina, Andrei Zeleneev
Date: 2023-06-26 00:00:00
Tasks: valid

When evaluating partial effects, it is important to distinguish between structural endogeneity and measurement errors. In contrast to linear models, these two sources of endogeneity affect partial effects differently in nonlinear models. We study this issue focusing on the Instrumental Variable (IV) Probit and Tobit models. We show that even when a valid IV is available, failing to differentiate between the two types of endogeneity can lead to either under- or over-estimation of the partial effects. We develop simple estimators of the bounds on the partial effects and provide easy to implement confidence intervals that correctly account for both types of endogeneity. We illustrate the methods in a Monte Carlo simulation and an empirical application.
Optimization of the Generalized Covariance Estimator in Noncausal Processes
Authors: Gianluca Cubadda, Francesco Giancaterini, Alain Hecq, Joann Jasiak
Date: 2023-06-26 00:00:00

This paper investigates the performance of the Generalized Covariance estimator (GCov) in estimating and identifying mixed causal and noncausal models. The GCov estimator is a semi-parametric method that minimizes an objective function without making any assumptions about the error distribution and is based on nonlinear autocovariances to identify the causal and noncausal orders. When the number and type of nonlinear autocovariances included in the objective function of a GCov estimator is insufficient/inadequate, or the error density is too close to the Gaussian, identification issues can arise. These issues result in local minima in the objective function, which correspond to parameter values associated with incorrect causal and noncausal orders. Then, depending on the starting point and the optimization algorithm employed, the algorithm can converge to a local minimum. The paper proposes the use of the Simulated Annealing (SA) optimization algorithm as an alternative to conventional numerical optimization methods. The results demonstrate that SA performs well when applied to mixed causal and noncausal models, successfully eliminating the effects of local minima. The proposed approach is illustrated by an empirical application involving a bivariate commodity price series.
Hybrid unadjusted Langevin methods for high-dimensional latent variable models
Authors: Ruben Loaiza-Maya, Didier Nibbering, Dan Zhu
Date: 2023-06-26 00:00:00

The exact estimation of latent variable models with big data is known to be challenging. The latents have to be integrated out numerically, and the dimension of the latent variables increases with the sample size. This paper develops a novel approximate Bayesian method based on the Langevin diffusion process. The method employs the Fisher identity to integrate out the latent variables, which makes it accurate and computationally feasible when applied to big data. In contrast to other approximate estimation methods, it does not require the choice of a parametric distribution for the unknowns, which often leads to inaccuracies. In an empirical discrete choice example with a million observations, the proposed method accurately estimates the posterior choice probabilities using only 2% of the computation time of exact MCMC.
Simple Estimation of Semiparametric Models with Measurement Errors
Authors: Kirill S. Evdokimov, Andrei Zeleneev
Date: 2023-06-25 00:00:00
Tasks: valid

We develop a practical way of addressing the Errors-In-Variables (EIV) problem in the Generalized Method of Moments (GMM) framework. We focus on the settings in which the variability of the EIV is a fraction of that of the mismeasured variables, which is typical for empirical applications. For any initial set of moment conditions our approach provides a "corrected" set of moment conditions that are robust to the EIV. We show that the GMM estimator based on these moments is root-n-consistent, with the standard tests and confidence intervals providing valid inference. This is true even when the EIV are so large that naive estimators (that ignore the EIV problem) are heavily biased with their confidence intervals having 0% coverage. Our approach involves no nonparametric estimation, which is especially important for applications with many covariates, and settings with multivariate or non-classical EIV. In particular, the approach makes it easy to use instrumental variables to address EIV in nonlinear models.
Latent Factor Analysis in Short Panels
Authors: Alain-Philippe Fortin, Patrick Gagliardini, Olivier Scaillet
Date: 2023-06-24 00:00:00
Tasks: Time Series

We develop inferential tools for latent factor analysis in short panels. The pseudo maximum likelihood setting under a large cross-sectional dimension n and a fixed time series dimension T relies on a diagonal TxT covariance matrix of the errors without imposing sphericity nor Gaussianity. We outline the asymptotic distributions of the latent factor and error covariance estimates as well as of an asymptotically uniformly most powerful invariant (AUMPI) test for the number of factors based on the likelihood ratio statistic. We derive the AUMPI characterization from inequalities ensuring the monotone likelihood ratio property for positive definite quadratic forms in normal variables. An empirical application to a large panel of monthly U.S. stock returns separates month after month systematic and idiosyncratic risks in short subperiods of bear vs. bull market based on the selected number of factors. We observe an uptrend in the paths of total and idiosyncratic volatilities while the systematic risk explains a large part of the cross-sectional total variance in bear markets but is not driven by a single factor. Rank tests show that observed factors struggle spanning latent factors with a discrepancy between the dimensions of the two factor spaces decreasing over time.
Decarbonization patterns of residential building operations in China and India
Authors: Ran Yan, Nan Zhou, Wei Feng, Minda Ma, Xiwang Xiang, Chao Mao
Date: 2023-06-24 00:00:00

As the two largest emerging emitters with the highest growth in operational carbon from residential buildings, the historical emission patterns and decarbonization efforts of China and India warrant further exploration. This study aims to be the first to present a carbon intensity model considering end-use performances, assessing the operational decarbonization progress of residential building in India and China over the past two decades using the improved decomposing structural decomposition approach. Results indicate (1) the overall operational carbon intensity increased by 1.4% and 2.5% in China and India, respectively, between 2000 and 2020. Household expenditure-related energy intensity and emission factors were crucial in decarbonizing residential buildings. (2) Building electrification played a significant role in decarbonizing space cooling (-87.7 in China and -130.2 kilograms of carbon dioxide (kgCO2) per household in India) and appliances (-169.7 in China and -43.4 kgCO2 per household in India). (3) China and India collectively decarbonized 1498.3 and 399.7 mega-tons of CO2 in residential building operations, respectively. In terms of decarbonization intensity, India (164.8 kgCO2 per household) nearly caught up with China (182.5 kgCO2 per household) in 2020 and is expected to surpass China in the upcoming years, given the country's robust annual growth rate of 7.3%. Overall, this study provides an effective data-driven tool for investigating the building decarbonization potential in China and India, and offers valuable insights for other emerging economies seeking to decarbonize residential buildings in the forthcoming COP28 age.
Heat increases experienced racial segregation in the United States
Authors: Till Baldenius, Nicolas Koch, Hannah Klauber, Nadja Klein
Date: 2023-06-01 00:00:00

Segregation on the basis of ethnic groups stands as a pervasive and persistent social challenge in many cities across the globe. Public spaces provide opportunities for diverse encounters but recent research suggests individuals adjust their time spent in such places to cope with extreme temperatures. We evaluate to what extent such adaptation affects racial segregation and thus shed light on a yet unexplored channel through which global warming might affect social welfare. We use large-scale foot traffic data for millions of places in 315 US cities between 2018 and 2020 to estimate an index of experienced isolation in daily visits between whites and other ethnic groups. We find that heat increases segregation. Results from panel regressions imply that a week with temperatures above 33{\deg}C in a city like Los Angeles induces an upward shift of visit isolation by 0.7 percentage points, which equals about 14% of the difference in the isolation index of Los Angeles to the more segregated city of Atlanta. The segregation-increasing effect is particularly strong for individuals living in lower-income areas and at places associated with leisure activities. Combining our estimates with climate model projections, we find that stringent mitigation policy can have significant co-benefits in terms of cushioning increases in racial segregation in the future.
Selling Multiple Complements with Packaging Costs
Authors: Simon Finster
Date: 2023-06-25 00:00:00

We consider a package assignment problem with multiple units of indivisible items. The seller specifies preferences over partitions of their supply between buyers as packaging costs. To express these preferences, we propose incremental costs together with a graph that defines cost interdependence. This facilitates using linear programming to find anonymous and package-linear Walrasian equilibrium prices. We provide necessary and sufficient conditions for the existence of Walrasian equilibria, as well as additional sufficient conditions. Furthermore, our cost framework ensures fair and transparent dual pricing and admits preferences over the concentration of allocated bundles in the market.