pwc-archive/papers-with-abstracts huggingface dataset:
Physics-Based Causal Lifting Linearization of Nonlinear Control Systems Underpinned by the Koopman Operator
Authors: Nicholas S. Selby, Filippos E. Sotiropoulos, H. Harry Asada
Date: 2021-08-24 00:00:00

Methods for constructing causal linear models from nonlinear dynamical systems through lifting linearization underpinned by Koopman operator and physical system modeling theory are presented. Outputs of a nonlinear control system, called observables, may be functions of state and input, $\phi(x,u)$. These input-dependent observables cannot be used for lifting the system because the state equations in the augmented space contain the time derivatives of input and are therefore anticausal. Here, the mechanism of creating anticausal observables is examined, and two methods for solving the causality problem in lifting linearization are presented. The first method is to replace anticausal observables by their integral variables $\phi^*$, and lift the dynamics with $\phi^*$, so that the time derivative of $\phi^*$ does not include the time derivative of input. The other method is to alter the original physical model by adding a small inertial element, or a small capacitive element, so that the system's causal relationship changes. These augmented dynamics alter the signal path from the input to the anticausal observable so that the observables are not dependent on inputs. Numerical simulations validate the effectiveness of the methods.
Comparing the Performance of Traffic Coordination Methods for Advanced Aerial Mobility
Authors: Ítalo Romani de Oliveira, Euclides C. Pinto Neto, Thiago Matsumoto, Huafeng Yu, Emiliano Bartolomé, Guillermo Frontera, Aaron Mayne
Date: 2021-08-24 00:00:00
Tasks: Management

Traffic Management in Advanced Aerial Mobility (AAM) inherits many elements of conventional Air Traffic Management (ATM), but brings new complexities and challenges of its own. One of its ways of guaranteeing separation is the use of airborne, stand-alone Detect-And-Avoid, an operational concept where each aircraft decides its avoidance maneuvers independently, observing right-of-way rules and, in specific implementations, some form of pairwise coordination. This is a fundamental safety element for autonomous aircraft but, according to our research, is not sufficient for high-density airspaces as envisioned for urban environments. In these environments, some way of explicit and strategic traffic coordination must be in place, as done for conventional ATM. For efficiency reasons, ATM is evolving to more flexible uses of the airspace, such that the use of dynamically allocated corridors is a rising concept for AAM. These strategic forms of traffic coordination are potentially highly efficient if the aircraft adhere to their trajectory contracts and there are no significant perturbations to the traffic. However, if significant perturbations occur, such as loss of data communication, or the sudden appearance of an intruder, a centralized system may not react appropriately in due time. In busy scenarios, even small deviations from plans may compound so rapidly as to result in large differences in the overall achieved scenario, resulting in congestions and convoluted conflicts. Therefore, it is worth studying traffic coordination techniques that work locally with shorter look-ahead times. To that end, we explore an airborne collaborative method for traffic coordination, which is capable of safely solving conflicts with multiple aircraft, stressing its capabilities throughout a large number of scenarios and comparing its performance with established methods.
Decentralized Air Traffic Management for Advanced Air Mobility
Authors: Ítalo Romani de Oliveira, Euclides Carlos Pinto Neto, Thiago Toshio Matsumoto, Huafeng Yu
Date: 2021-08-24 00:00:00
Tasks: Diversity, Management

Leading proposals for Advanced Air Mobility (AAM) recognize the need for strategic and tactical airspace deconfliction, where the need for coordination appears in different forms and nuances. One recurring element is the use of pre-defined airways or corridors, a natural way to create order, with proven success from the conventional, manned, Air Traffic Management (ATM). But, while ATM is evolving to 4D Trajectory-Based Operations, when we apply the ATM principles to very dense and potentially more complex operations such as AAM, we have to consider their limitations in such demanding new environment. The requirement of following circulation corridors most often increase flight distance and inevitably create bottlenecks, hence we explore the hypothesis of not using corridors, testing such option via a simple and scalable simulation model. Other motivations for comparing different forms of traffic coordination are redundancy and diversity, which have potential to increase system safety. Relying on a single method to maintain traffic separation of course would not be allowed in practice. However, the concepts that we have seen so far leave a gap between two very distinct and co-existing methods: one, cooperative and centered on a ground-based Provider of Services for UAM (PSU), and another, which is mostly non-cooperative and independent, centered on the individual aircraft, commonly referred to as Detect-And-Avoid (DAA). This duality achieves a welcomed diversity, however presents several points for improvement. In-between these opposite methods, this paper analyzes the performance of an airborne cooperative method to coordinate traffic which is capable of safely solving conflicts of multiple aircraft (more than two) and achieve higher efficiency than DAA alone, thus with potential for being an alternative or a live fallback for ground-based traffic coordination.
Adaptive Extended Kalman Filter (ROSE-Filter) for Positioning System
Authors: Reiner Marchthaler
Date: 2021-08-24 00:00:00
Tasks: Position

This paper illustrates the way for estimating position and orientation of a vehicle with an Extended Kalman Filter (EKF). For this purpose a non-linear model is designed and an adaptive calculation of measurement noise covariance matrix is used, a so called ROSE-Filter (Rapid Ongoing Stochastic covariance Estimation-Filter). Input of the system is the measured position from a two dimensional position system. Estimated is the pose (position and orientation) and the velocity of the vehicle.
A multimodal solution approach for mitigating the impact of planned maintenance on metro rail attractiveness
Authors: A lice Consilvio, Lorenzo Calabrò, Angela Di Febbraro, Nicola Sacco
Date: 2021-08-24 00:00:00

The possible unavailability of urban rail-based transport services due to planned maintenance activities may have significant consequences on the perceived quality of service, thus affecting railway attractiveness. To cope with the mitigation of planned service interruptions and to guarantee a seamless journey and a good travel experience for passengers, it is possible to exploit the existing services differently and/or provide additional on-demand services, such as temporary supplemental bus lines. In this context, this paper aims to develop a mathematical programming model for planning service interruptions due to maintenance considering passenger transport demand dynamics. In particular, the proposed approach deals with service interruptions characterized by a long duration for which timetable adaption strategies are not applicable, suggesting mitigation actions that exploit the already existing services and/or the activation of additional ones, with the aim of minimizing users' inconvenience. In doing so, the planned infrastructure status (i.e., available or under maintenance), as well as the forecasted transport demand, are taken into account to adapt the service accordingly by offering a multimodal transport solution to passengers. To find the best solution, a decomposition solution approach is proposed in combination with a multistage cooperative framework with feedback that models the negotiation process between the involved actors. Finally, the applicability of the proposed approach to real case studies is discussed based on some performance indicators.
Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale
Authors: Paul Hünermund, Beyers Louw, Itamar Caspi
Date: 2021-08-25 00:00:00
Tasks: BIG-bench Machine Learning, Model Selection, Variable Selection

Double machine learning (DML) has become an increasingly popular tool for automated variable selection in high-dimensional settings. Even though the ability to deal with a large number of potential covariates can render selection-on-observables assumptions more plausible, there is at the same time a growing risk that endogenous variables are included, which would lead to the violation of conditional independence. This paper demonstrates that DML is very sensitive to the inclusion of only a few "bad controls" in the covariate space. The resulting bias varies with the nature of the theoretical causal model, which raises concerns about the feasibility of selecting control variables in a data-driven way.
Refugees and Host State Security: An Empirical Investigation of Rohingya Refuge in Bangladesh
Authors: Sarwar J. Minar
Date: 2021-08-25 00:00:00

Conventional wisdom suggests that large-scale refugees pose security threats to the host community or state. With massive influx of Rohingyas in Bangladesh in 2017 resulting a staggering total of 1.6 million Rohingyas, a popular discourse emerged that Bangladesh would face severe security threats. This article investigates the security experience of Bangladesh in case of Rohingya influx over a three-year period, August 2017 to August 2020. The research question I intend to address is, 'has Bangladesh experienced security threat due to massive Rohingya influx?' If so in what ways? I test four security threat areas: societal security, economic security, internal security, and public security. I have used newspaper content analysis over past three years along with interview data collected from interviewing local people in coxs bazar area where the Rohingya camps are located. To assess if the threats are low level, medium level, or high level, I investigated both the frequency of reports and the way they are interpreted. I find that Bangladesh did not experience any serious security threats over the last three years. There are some criminal activities and offenses, but these are only low-level security threat at best. My research presents empirical evidence that challenges conventional assertions that refugees are security threats or challenges to the host states.
Prevention of Terrorist Crimes in the North Caucasus Region
Authors: Ivan Kucherkov, Mattia Masolletti
Date: 2021-08-25 00:00:00

The relevance of the topic is dictated by the fact that in recent decades, the threat to international security emanating from terrorism has increased many times. Terrorist organizations have become full-fledged subjects of politics on a par with political parties. In addition, enormous power and resources are concentrated in the hands of terrorist groups. Terrorist activity has become the usual way of leading a political struggle, expressing social protest. In addition, terrorism has become a tool in economic competition. Each terrorist action entails more and more human casualties. It breeds instability, fear, hatred, and distrust in society. The authors pay special attention to counter-terrorism activities in the North Caucasus Region.
Influential News and Policy-making
Authors: Federico Vaccari
Date: 2021-08-25 00:00:00

It is believed that interventions that change the media's costs of misreporting can increase the information provided by media outlets. This paper analyzes the validity of this claim and the welfare implications of those types of interventions that affect misreporting costs. I study a model of communication between an uninformed voter and a media outlet that knows the quality of two competing candidates. The alternatives available to the voter are endogenously championed by the two candidates. I show that higher costs may lead to more misreporting and persuasion, whereas low costs result in full revelation; interventions that increase misreporting costs never harm the voter, but those that do so slightly may be wasteful of public resources. I conclude that intuitions derived from the interaction between the media and voters, without incorporating the candidates' strategic responses to the media environment, do not capture properly the effects of these types of interventions.
Crypto Wash Trading
Authors: Lin William Cong, Xi Li, Ke Tang, Yang Yang
Date: 2021-08-24 00:00:00

We introduce systematic tests exploiting robust statistical and behavioral patterns in trading to detect fake transactions on 29 cryptocurrency exchanges. Regulated exchanges feature patterns consistently observed in financial markets and nature; abnormal first-significant-digit distributions, size rounding, and transaction tail distributions on unregulated exchanges reveal rampant manipulations unlikely driven by strategy or exchange heterogeneity. We quantify the wash trading on each unregulated exchange, which averaged over 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and userbase), market conditions, and regulation.
Financing Entrepreneurship and Innovation in China
Authors: Lin William Cong, Charles M. C. Lee, Yuanyu Qu, Tao Shen
Date: 2021-08-24 00:00:00
Tasks: Survey

This study reports on the current state-of-affairs in the funding of entrepreneurship and innovations in China and provides a broad survey of academic findings on the subject. We also discuss the implications of these findings for public policies governing the Chinese financial system, particularly regulations governing the initial public offering (IPO) process. We also identify and discuss promising areas for future research.
Single-peaked domains with designer uncertainty
Authors: Aroon Narayanan
Date: 2021-08-25 00:00:00

This paper studies single-peaked domains where the designer is uncertain about the underlying alignment according to which the domain is single-peaked. The underlying alignment is common knowledge amongst agents, but preferences are private knowledge. Thus, the state of the world has both a public and private element, with the designer uninformed of both. I first posit a relevant solution concept called implementation in mixed information equilibria, which requires Nash implementation in the public information and dominant strategy implementation in the private information given the public information. I then identify necessary and sufficient conditions for social rules to be implementable. The characterization is used to identify unanimous and anonymous implementable social rules for various belief structures of the designer, which basically boils down to picking the right rules from the large class of median rules identified by Moulin (1980), and hence this result can be seen as identifying which median rules are robust to designer uncertainty.
Spatial Fuzzy Clustering on Synthetic Aperture Radar Images to Detect Changes
Authors: Necmettin Bayar, W. T Al-Shaibani, Ibraheem Shayea, Abdulkader Taha, Azizul Azizan
Date: 2021-08-25 00:00:00
Tasks: Change Detection, Clustering

Data and data sources have become increasingly essential in recent decades. Scientists and researchers require more data to deploy AI approaches as the field continues to improve. In recent years, the rapid technological advancements have had a significant impact on human existence. One major field for collecting data is satellite technology. With the fast development of various satellite sensor equipment, synthetic aperture radar (SAR) images have become an important source of data for a variety of research subjects, including environmental studies, urban studies, coastal extraction, water sources, etc. Change detection and coastline detection are both achieved using SAR pictures. However, speckle noise is a major problem in SAR imaging. Several solutions have been offered to address this issue. One solution is to expose SAR images to spatial fuzzy clustering. Another solution is to separate speech. This study utilises the spatial function to overcome speckle noise and cluster the SAR images with the highest achieved accuracy. The spatial function is proposed in this work since the likelihood of data falling into one cluster is what this function is all about. When the spatial function is employed to cluster data in fuzzy logic, the clustering outcomes improve. The proposed clustering technique is us
Airplane Type Identification Based on Mask RCNN; An Approach to Reduce Airport Traffic Congestion
Authors: WT Al-Shaibani, Mustafa Helvaci, Ibraheem Shayea, Azizul Azizan
Date: 2021-08-25 00:00:00
Tasks: Object, Object Recognition

One of the most difficult jobs in remote sensing is dealing with traffic bottlenecks at airports. This fact has been confirmed by several studies attempting to resolve this issue. Among a wide range of approaches employed the most successful methods have been based on airplane object recognition using satellite images as datasets for deep learning models. Airplane object identification is not a viable method for resolving traffic congestion. There are several types of airplanes at the airport each with its own set of requirements and specifications.Utilizing satellite pictures will require the use of complex equipment which is a financial burden. In this work a universal low-cost and efficient solution for airport traffic congestion is proposed. Drone-captured aerial pictures are used to train and assess a Mask Region Convolution Neural Network model. This model can detect the presence of any aircraft in a photograph and pinpoint its location. It also includes mask estimations to properly identify each detected airplane type based on the estimated surface area and cabin length which are crucial variables in distinguishing planes. The study is conducted using Microsoft Common Object in Context COCO metrics average accuracies and a confusion matrix all of which demonstrate the approach's potential in providing valuable aid for dealing with traffic congestion at airports.
Non-Parametric Quickest Mean Change Detection
Authors: Yuchen Liang, Venugopal V. Veeravalli
Date: 2021-08-25 00:00:00
Tasks: Change Detection

The problem of quickest detection of a change in the mean of a sequence of independent observations is studied. The pre-change distribution is assumed to be stationary, while the post-change distributions are allowed to be non-stationary. The case where the pre-change distribution is known is studied first, and then the extension where only the mean and variance of the pre-change distribution are known. No knowledge of the post-change distributions is assumed other than that their means are above some pre-specified threshold larger than the pre-change mean. For the case where the pre-change distribution is known, a test is derived that asymptotically minimizes the worst-case detection delay over all possible post-change distributions, as the false alarm rate goes to zero. Towards deriving this asymptotically optimal test, some new results are provided for the general problem of asymptotic minimax robust quickest change detection in non-stationary settings. Then, the limiting form of the optimal test is studied as the gap between the pre- and post-change means goes to zero, called the Mean-Change Test (MCT). It is shown that the MCT can be designed with only knowledge of the mean and variance of the pre-change distribution. The performance of the MCT is also characterized when the mean gap is moderate, under the additional assumption that the distributions of the observations have bounded support. The analysis is validated through numerical results for detecting a change in the mean of a beta distribution. The use of the MCT in monitoring pandemics is also demonstrated.
What the odor is not: Estimation by elimination
Authors: Vijay Singh, Martin Tchernookov, Vijay Balasubramanian
Date: 2019-03-06 00:00:00

Olfactory systems use a small number of broadly sensitive receptors to combinatorially encode a vast number of odors. We propose a method of decoding such distributed representations by exploiting a statistical fact: receptors that do not respond to an odor carry more information than receptors that do because they signal the absence of all odorants that bind to them. Thus, it is easier to identify what the odor is not, rather than what the odor is. For realistic numbers of receptors, response functions, and odor complexity, this method of elimination turns an underconstrained decoding problem into a solvable one, allowing accurate determination of odorants in a mixture and their concentrations. We construct a neural network realization of our algorithm based on the structure of the olfactory pathway.
An Electroencephalography connectome predictive model of major depressive disorder severity
Authors: Aya Kabbara, Gabriel Robert, Mohamad Khalil, Marc Verin, Pascal Benquet, Mahmoud Hassan
Date: 2021-08-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N=328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r=0.61, p=4 x 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1=53, N2=154). Results showed high significant correlations between the predicted and the measured depression scale scores (r1= 0.49, r2=0.37, p<0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.
The Impact of Mobility between Rural Areas and Forests on the Spread of Zika
Authors: Kifah Al-Maqrashi, Fatma Al-Musalhi, Ibrahim M. Elmojtaba, Nasser Al-Salti
Date: 2021-08-24 00:00:00

A mathematical model of Zika virus transmission incorporating human movement between rural areas and nearby forests is presented to investigate the role of human movement in the spread of Zika virus infections in human and mosquito populations. Proportions of both susceptible and infected humans living in rural areas are assumed to move to nearby forest areas. Direct, indirect and vertical transmission routes are incorporated for all populations. Mathematical analysis of the proposed model has been presented. The analysis starts with normalizing the proposed model. Positivity and boundedness of solutions to the normalized model have been then addressed. The basic reproduction number has been calculated using the next generation matrix method and its relation to the three routes of disease transmission has been presented. The sensitivity analysis of the basic reproduction number to all model parameters has been investigated. The analysis also includes existence and stability of disease free and endemic equilibrium points. Bifurcation analysis has been also carried out. Finally, numerical solutions to the normalized model have been obtained to confirm the theoretical results and to demonstrate the impact of human movement in the disease transmission in human and mosquito populations.