pwc-archive/papers-with-abstracts huggingface dataset:
Backup Plan Constrained Model Predictive Control with Guaranteed Stability
Authors: Ran Tao, Hunmin Kim, Hyung-Jin Yoon, Wenbin Wan, Naira Hovakimyan, Lui Sha, Petros Voulgaris
Date: 2023-06-09 00:00:00
Tasks: Autonomous Vehicles, Computational Efficiency, Model Predictive Control

This article proposes and evaluates a new safety concept called backup plan safety for path planning of autonomous vehicles under mission uncertainty using model predictive control (MPC). Backup plan safety is defined as the ability to complete an alternative mission when the primary mission is aborted. To include this new safety concept in control problems, we formulate a feasibility maximization problem aiming to maximize the feasibility of the primary and alternative missions. The feasibility maximization problem is based on multi-objective MPC, where each objective (cost function) is associated with a different mission and balanced by a weight vector. Furthermore, the feasibility maximization problem incorporates additional control input horizons toward the alternative missions on top of the control input horizon toward the primary mission, denoted as multi-horizon inputs, to evaluate the cost for each mission. We develop the backup plan constrained MPC algorithm, which designs the weight vector that ensures asymptotic stability of the closed-loop system, and generates the optimal control input by solving the feasibility maximization problem with computational efficiency. The performance of the proposed algorithm is validated through simulations of a UAV path planning problem.
Model Predictive Control for Integrated Lateral Stability
Authors: Jad Yahya, Siddharth Saha, Haoru Xue, Allen Y. Yang
Date: 2023-06-09 00:00:00
Tasks: model, Model Predictive Control

This paper studies the design of a Model Predictive Controller (MPC) for integrated lateral stability, traction/braking control, and rollover prevention of electric vehicles intended for very high speed (VHS) racing applications. We first identify the advantages of a state-of-the-art dynamic model in that it includes rollover prevention into the MPC (a total of 8 states) and also linearizes the tire model prior to solving the MPC problem to save computation time. Then the design of a novel model predictive controller for lateral stability control is proposed aimed for achieving stable control at top speed significantly greater than typical highway speed limits. We have tested the new solution in simulation environments associated with the Indy Autonomous Challenge, where its real-world racing conditions include significant road banking angles, lateral position tracking, and a different suspension model of its Dallara Indy Lights chassis. The results are very promising with a low solver time in Python, as low as 50 Hz, and a lateral error of 30 cm at speeds of 45 m/s. Our open source code is available at: https: //github.com/jadyahya/Roll-Yaw-and-Lateral-Velocity-MPC/.
Nonlinear Stability Assessment Of Type-4 Wind Turbines During Unbalanced Grid Faults Based On Reduced-Order Model
Authors: Sujay Ghosh, Mohammad Kazem Bakhshizadeh, Łukasz Kocewiak, Guangya Yang
Date: 2023-06-09 00:00:00

As the number of converter-based renewable generations in the power system is increasing, the inertia provided by the synchronous generators is reducing, which in turn is reducing the stability margins of the power system. In order to assess the large-signal stability, it is essential to model the wind power plant connections accurately. However, the actual EMT models are often unavailable, black-boxed, or computationally too heavy to model in detail. Hence, simplified reduced-order models (ROMs) resembling the actual system behaviour have gained prominence in stability studies. In this regard, an improved WT ROM was proposed to investigate large signal stability during unbalanced grid faults. The methodology presents a systematic way to model the coupled sequence components of the WT ROM for various grid faults. Based on the studies carried out in this paper, it is observed that post unbalanced grid disturbances the proposed WT ROM correctly tracks the angle and frequency, and its trajectory is a good match when compared to a detailed simulation model in PSCAD.
Encrypted Simultaneous Control of Joint Angle and Stiffness of Antagonistic Pneumatic Artificial Muscle Actuator by Polynomial Approximation
Authors: Yuta Takeda, Takaya Shin, Kaoru Teranishi, Kiminao Kogiso
Date: 2023-06-09 00:00:00

This study proposes an encrypted simultaneous control system for an antagonistic pneumatic artificial muscle (PAM) actuator toward developing a cybersecure and flexible actuator. First, a novel simultaneous control system design is considered for the joint angle and stiffness of a PAM actuator in a model-based design approach, facilitating the use of an encrypted control method. The designed controller includes a contraction force model expressed as rational polynomial functions, which makes it difficult to encrypt the controller. To overcome this difficulty, a least absolute shrinkage and selection operator (LASSO)-based polynomial approximation is employed for a rational controller. The resulting polynomial controller is then transformed into a matrix-vector product form, which enables the use of a specific homomorphic encryption scheme to develop an encrypted simultaneous control system for the PAM actuator. Finally, this study quantitatively evaluates the tracking control performance of the original, approximated, and encrypted controllers. The experimental results show that the proposed encrypted controller achieves simultaneous tracking of the joint angle and stiffness with a tracking error of less than 2.7 %.
Risk-aware Urban Air Mobility Network Design with Overflow Redundancy
Authors: Qinshuang Wei, Zhenyu Gao, John-Paul Clarke, Ufuk Topcu
Date: 2023-06-08 00:00:00
Tasks: Management

Urban air mobility (UAM), as envisioned by aviation professionals, will transport passengers and cargo at low altitudes within urban and suburban areas. To operate in urban environments, precise air traffic management, in particular the management of traffic overflows due to physical and operational disruptions will be critical to ensuring system safety and efficiency. To this end, we propose UAM network design with reserve capacity, i.e., a design where alternative landing options and flight corridors are explicitly considered as a means of improving contingency management. Similar redundancy considerations are incorporated in the design of many critical infrastructures, yet remain unexploited in the air transportation literature. In our methodology, we first model how disruptions to a given UAM network might impact on the nominal traffic flow and how this flow might be re-accommodated on an extended network with reserve capacity. Then, through an optimization problem, we select the locations and capacities for the backup vertiports with the maximal expected throughput of the extended network over all possible disruption scenarios, while the throughput is the maximal amount of flights that the network can accommodate per unit of time. We show that we can obtain the solution for the corresponding bi-level and bi-linear optimization problem by solving a mixed-integer linear program. We demonstrate our methodology in the case study using networks from Milwaukee, Atlanta, and Dallas--Fort Worth metropolitan areas and show how the throughput and flexibility of the UAM networks with reserve capacity can outcompete those without.
Theoretical foundations of studying criticality in the brain
Authors: Yang Tian, Zeren Tan, Hedong Hou, Guoqi Li, Aohua Cheng, Yike Qiu, Kangyu Weng, Chun Chen, Pei Sun
Date: 2023-06-09 00:00:00

Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information processing capacities in the brain. While considerable evidence generally supports this hypothesis, non-negligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, i.e., ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistic techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions.
Deep Learning for Day Forecasts from Sparse Observations
Authors: Marcin Andrychowicz, Lasse Espeholt, Di Li, Samier Merchant, Alexander Merose, Fred Zyda, Shreya Agrawal, Nal Kalchbrenner
Date: 2023-06-06 00:00:00
Tasks: Deep Learning

Deep neural networks offer an alternative paradigm for modeling weather conditions. The ability of neural models to make a prediction in less than a second once the data is available and to do so with very high temporal and spatial resolution, and the ability to learn directly from atmospheric observations, are just some of these models' unique advantages. Neural models trained using atmospheric observations, the highest fidelity and lowest latency data, have to date achieved good performance only up to twelve hours of lead time when compared with state-of-the-art probabilistic Numerical Weather Prediction models and only for the sole variable of precipitation. In this paper, we present MetNet-3 that extends significantly both the lead time range and the variables that an observation based neural model can predict well. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 introduces a key densification technique that implicitly captures data assimilation and produces spatially dense forecasts in spite of the network training on extremely sparse targets. MetNet-3 has a high temporal and spatial resolution of, respectively, up to 2 minutes and 1 km as well as a low operational latency. We find that MetNet-3 is able to outperform the best single- and multi-member NWPs such as HRRR and ENS over the CONUS region for up to 24 hours ahead setting a new performance milestone for observation based neural models. MetNet-3 is operational and its forecasts are served in Google Search in conjunction with other models.
The speckle contrast extended to the polarimetric case: applications to radar and Laser images
Authors: Elise Colin
Date: 2023-06-08 00:00:00
Tasks: Time Series

This article proposes the application of various alternative definitions of the multivariate coefficient of variation parameter in two domains: radar polarimetric time series and dynamic polarimetric speckle. In the first case, the focus is on detecting permanent scatterers or changes, while in the second case, it involves calculating activity images. Our study demonstrates that most of these parameters offer added value in terms of signal-to-noise ratio improvement and enhancing contrast in specific regions. Furthermore, the concept of polarimetric multivariate coefficient of variation proves to be closely related to the degree of polarization.
Random matrix theory and nested clustered portfolios on Mexican markets
Authors: Andrés García-Medina, Benito Rodriguéz-Camejo
Date: 2023-06-09 00:00:00

This work aims to deal with the optimal allocation instability problem of Markowitz's modern portfolio theory in high dimensionality. We propose a combined strategy that considers covariance matrix estimators from Random Matrix Theory~(RMT) and the machine learning allocation methodology known as Nested Clustered Optimization~(NCO). The latter methodology is modified and reformulated in terms of the spectral clustering algorithm and Minimum Spanning Tree~(MST) to solve internal problems inherent to the original proposal. Markowitz's classical mean-variance allocation and the modified NCO machine learning approach are tested on financial instruments listed on the Mexican Stock Exchange~(BMV) in a moving window analysis from 2018 to 2022. The modified NCO algorithm achieves stable allocations by incorporating RMT covariance estimators. In particular, the allocation weights are positive, and their absolute value adds up to the total capital without considering explicit restrictions in the formulation. Our results suggest that can be avoided the risky \emph{short position} investment strategy by means of RMT inference and statistical learning techniques.
Localized Neural Network Modelling of Time Series: A Case Study on US Monetary Policy
Authors: Jiti Gao, Fei Liu, Bin Peng, Yanrong Yang
Date: 2023-06-08 00:00:00
Tasks: Time Series, valid, Variable Selection

In this paper, we investigate a semiparametric regression model under the context of treatment effects via a localized neural network (LNN) approach. Due to a vast number of parameters involved, we reduce the number of effective parameters by (i) exploring the use of identification restrictions; and (ii) adopting a variable selection method based on the group-LASSO technique. Subsequently, we derive the corresponding estimation theory and propose a dependent wild bootstrap procedure to construct valid inferences accounting for the dependence of data. Finally, we validate our theoretical findings through extensive numerical studies. In an empirical study, we revisit the impacts of a tightening monetary policy action on a variety of economic variables, including short-/long-term interest rate, inflation, unemployment rate, industrial price and equity return via the newly proposed framework using a monthly dataset of the US.
An Empirical Analysis of the Effect of Ballot Truncation on Ranked-Choice Electoral Outcomes
Authors: Mallory Dickerson, Erin Martin, David McCune
Date: 2023-06-09 00:00:00

In ranked-choice elections voters cast preference ballots which provide a voter's ranking of the candidates. The method of ranked-choice voting (RCV) chooses a winner by using voter preferences to simulate a series of runoff elections. Some jurisdictions which use RCV limit the number of candidates that voters can rank on the ballot, imposing what we term a truncation level, which is the number of candidates that voters are allowed to rank. Given fixed voter preferences, the winner of the election can change if we impose different truncation levels. We use a database of 1171 real-world ranked-choice elections to empirically analyze the potential effects of imposing different truncation levels in ranked-choice elections. Our general finding is that if the truncation level is at least three then restricting the number of candidates which can be ranked on the ballot rarely affects the election winner.
The Relationship Between Burnout Operators with the Functions of Family Tehran Banking Melli Iran Bank in 2015
Authors: Mohammad Heydari, Matineh Moghaddam, Habibollah Danai
Date: 2023-06-09 00:00:00
Tasks: Descriptive

In this study, the relationship between burnout and family functions of the Melli Iran Bank staff will be studied. A number of employees within the organization using appropriate scientific methods as the samples were selected by detailed questionnaire and the appropriate data is collected burnout and family functions. The method used descriptive statistical population used for this study consisted of 314 bank loan officers in branches of Melli Iran Bank of Tehran province and all the officials at the bank for >5 years of service at Melli Iran Bank branches in Tehran. They are married and men constitute the study population. The Maslach Burnout Inventory in the end internal to 0/90 alpha emotional exhaustion, depersonalization and low personal accomplishment Cronbach alpha of 0/79 and inventory by 0/71 within the last family to solve the problem 0/70, emotional response 0/51, touch 0/70, 0/69 affective involvement, roles, 0/59, 0/68 behavior is controlled. The results indicate that the hypothesis that included the relationship between burnout and 6, the family functioning, problem solving, communication, roles, affective responsiveness, affective fusion there was a significant relationship between behavior and the correlation was negative. The burnout is high; the functions within the family will be in trouble.
Interbank Decisions and Margins of Stability: an Agent-Based Stock-Flow Consistent Approach
Authors: Jessica Reale
Date: 2023-06-09 00:00:00

This study investigates the functioning of modern payment systems through the lens of banks' maturity mismatch practices, and it examines the effects of banks' refusal to roll over short-term interbank liabilities on financial stability. Within an agent-based stock-flow consistent framework, banks can engage in two segments of the interbank market that differ in maturity, overnight and term. We compare two interbank matching scenarios to assess how bank-specific maturity targets, dependent on the dictates of the Net Stable Funding Ratio, impact the dynamics of the interbank market and the effectiveness of conventional monetary policies. The findings reveal that maturity misalignment between deficit and surplus banks compromises the interbank market's efficiency and increases reliance on the central bank's standing facilities. Monetary policy interest-rate steering practices also become less effective. The study also uncovers a dual stability-based configuration in the banking sector, resembling the segmented European interbank structure. This paper suggests that heterogeneous maturity mismatches between surplus and deficit banks may result in asymmetric funding frictions that might precede credit- and sovereign-risk explanations of interbank tensions. Also, a combined examination of macroprudential tools and rollover-based interbank dynamics can enhance our understanding of how regulatory changes impact the stability of heterogeneous banking sectors.
The far-reaching effects of bombing on fertility in mid-20th century Japan
Authors: Tatsuki Inoue amd Erika Igarashi
Date: 2023-06-09 00:00:00

This study explores the indirect impact of war damage on postwar fertility, with a specific focus on Japan's air raids during World War II. Using 1935 and 1947 Kinki region town/village data and city air raid damage information, we explored the "far-reaching effects" on fertility rates in nearby undamaged areas. Our fixed-effects model estimates show that air raids influenced postwar fertility within a 15-kilometer radius of the bombed cities. These impacts varied based on bombing intensity, showing both positive and negative effects. Moreover, a deeper analysis, using the Allied flight path as a natural experiment, indicates that air raid threats and associated fear led to increased postwar fertility, even in undamaged areas. This study highlights the relatively unexplored indirect consequences of war on fertility rates in neighboring regions and significantly contributes to the literature on the relationship between wars and fertility.
Comparing Regularized Kelvinlet Functions and the Finite Element Method for Registration of Medical Images to Sparse Organ Data
Authors: Morgan Ringel, Jon Heiselman, Winona Richey, Ingrid Meszoely, William Jarnagin, Michael Miga
Date: 2023-06-08 00:00:00
Tasks: Decision Making

Image-guided surgery collocates patient-specific data with the physical environment to facilitate surgical decision making in real-time. Unfortunately, these guidance systems commonly become compromised by intraoperative soft-tissue deformations. Nonrigid image-to-physical registration methods have been proposed to compensate for these deformations, but intraoperative clinical utility requires compatibility of these techniques with data sparsity and temporal constraints in the operating room. While linear elastic finite element models are effective in sparse data scenarios, the computation time for finite element simulation remains a limitation to widespread deployment. This paper proposes a registration algorithm that uses regularized Kelvinlets, which are analytical solutions to linear elasticity in an infinite domain, to overcome these barriers. This algorithm is demonstrated and compared to finite element-based registration on two datasets: a phantom dataset representing liver deformations and an in vivo dataset representing breast deformations. The regularized Kelvinlets algorithm resulted in a significant reduction in computation time compared to the finite element method. Accuracy as evaluated by target registration error was comparable between both methods. Average target registration errors were 4.6 +/- 1.0 and 3.2 +/- 0.8 mm on the liver dataset and 5.4 +/- 1.4 and 6.4 +/- 1.5 mm on the breast dataset for the regularized Kelvinlets and finite element method models, respectively. This work demonstrates the generalizability of using a regularized Kelvinlets registration algorithm on multiple soft tissue elastic organs. This method may improve and accelerate registration for image-guided surgery applications, and it shows the potential of using regularized Kelvinlets solutions on medical imaging data.
Feature Selection on Sentinel-2 Multi-spectral Imagery for Efficient Tree Cover Estimation
Authors: Usman Nazir, Momin Uppal, Muhammad Tahir, Zubair Khalid
Date: 2023-05-31 00:00:00
Tasks: feature selection, Management

This paper proposes a multi-spectral random forest classifier with suitable feature selection and masking for tree cover estimation in urban areas. The key feature of the proposed classifier is filtering out the built-up region using spectral indices followed by random forest classification on the remaining mask with carefully selected features. Using Sentinel-2 satellite imagery, we evaluate the performance of the proposed technique on a specified area (approximately 82 acres) of Lahore University of Management Sciences (LUMS) and demonstrate that our method outperforms a conventional random forest classifier as well as state-of-the-art methods such as European Space Agency (ESA) WorldCover 10m 2020 product as well as a DeepLabv3 deep learning architecture.