pwc-archive/papers-with-abstracts huggingface dataset:
Global Structure Identifiability and Reconstructibility of an NDS with Descriptor Subsystems
Authors: Tong Zhou, Kailin Yin
Date: 2021-07-02 00:00:00

This paper investigates requirements on a networked dynamic system (NDS) such that its subsystem interactions can be solely determined from experiment data or reconstructed from its overall model. The NDS is constituted from several subsystems whose dynamics are described through a descriptor form. Except regularity on each subsystem and the whole NDS, no other restrictions are put on either subsystem dynamics or subsystem interactions. A matrix rank based necessary and sufficient condition is derived for the global identifiability of subsystem interactions, which leads to several conclusions about NDS structure identifiability when there is some a priori information. This matrix also gives an explicit description for the set of subsystem interactions that can not be distinguished from experiment data only. In addition, under a well-posedness assumption, a necessary and sufficient condition is obtained for the reconstructibility of subsystem interactions from an NDS descriptor form model. This condition can be verified with each subsystem separately and is therefore attractive in the analysis and synthesis of a large-scale NDS. Simulation results show that rather than increases monotonically with the distance of subsystem interactions to the undifferentiable set, the magnitude of the external output differences between two NDSs with distinct subsystem interactions increases much more rapidly when one of them is close to be unstable. In addition, directions of probing signals are also very important in distinguishing external outputs of distinctive NDSs.These findings are expected to be helpful in identification experiment designs, etc.
Identification of NARX Models for Compensation Design
Authors: Lucas A. Tavares, Petrus E. O. G. B. Abreu, Luis A. Aguirre
Date: 2020-11-19 00:00:00

This report presents the modeling results for three systems, two numerical and one experimental. In the numerical examples, we use mathematical models previously obtained in the literature as the systems to be identified. The first numerical example is a heating system with a polynomial nonlinearity that is described by a Hammerstein model. The second is a Bouc-Wen model that represents the hysteretic behavior in a piezoelectric actuator. Finally, the experimental example is a pneumatic valve that presents a variety of nonlinearities, including hysteresis. For each example, a Nonlinear AutoRegressive model with eXogenous inputs (NARX) is identified using two well-established techniques together, the Error Reduction Ratio (ERR) method to hierarchically select the regressors and the Akaike's Information Criterion (AIC) to truncate the number of terms. Using both approaches, the structure selection is achieved. The design of the excitation input is based on preserving the frequencies of interest and force the system to achieve different points of operation. Hence, having the structure previously selected with ERR and AIC, we use the Extended Least Squares (ELS) algorithm to estimate the parameters. The results show that it is possible to identify the referred systems with no more than five terms. These identified models will be used for nonlinearity compensation in future works.
Intelligent Momentary Assisted Control for Autonomous Emergency Braking
Authors: Konstantinos Gounis, Nick Bassiliades
Date: 2021-07-02 00:00:00
Tasks: Collision Avoidance, Friction

Development of control algorithms for enhancing performance in safety-critical systems such as the Autonomous Emergency Braking system (AEB) is an important issue in the emerging field of automated electric vehicles. In this study, we design a safety distance-based hierarchical AEB control system constituted of a high-level Rule-Based Supervisory control module, an intermediate-level switching algorithm and a low-level control module. The Rule Based supervisor determines the required deceleration command that is fed to the low-level control module via the switching algorithm. In the low-level, two wheel slip control algorithms were developed, a Robust Sliding Mode controller and a Gain-Scheduled Linear Quadratic Regulator. For the needs of this control design, a non-linear dynamic vehicle model was implemented whereas a constant tire-road friction coefficient was considered. The proposed control system was validated in Simulink, assuming a straight-line braking maneuver on a flat dry road. The simulation results demonstrated satisfactory emergency braking performance with full collision avoidance in both proposed control system combinations.
Temporal Analysis of Worldwide War
Authors: Devansh Bajpai, Rishi Ranjan Singh
Date: 2021-06-27 00:00:00

Analysis of wars and conflicts between regions has been an important topic of interest throughout the history of humankind. In the latter part of the 20th century, in the aftermath of two World Wars and the shadow of nuclear, biological, and chemical holocaust, more was written on the subject than ever before. Wars have a negative impact on a country's economy, social order, infrastructure, and public health. In this paper, we study the wars fought in history and draw conclusions from that. We explore the participation of countries in wars and the nature of relationships between various countries during different timelines. A big part of today's wars is fought against terrorism. Therefore, this study also attempts to shed light on different countries' exposure to terrorist encounters and analyses the impact of wars on a country's economy in terms of change in GDP.
Partial Identification and Inference in Duration Models with Endogenous Censoring
Authors: Shosei Sakaguchi
Date: 2021-07-02 00:00:00

This paper studies identification and inference in transformation models with endogenous censoring. Many kinds of duration models, such as the accelerated failure time model, proportional hazard model, and mixed proportional hazard model, can be viewed as transformation models. We allow the censoring of a duration outcome to be arbitrarily correlated with observed covariates and unobserved heterogeneity. We impose no parametric restrictions on either the transformation function or the distribution function of the unobserved heterogeneity. In this setting, we develop bounds on the regression parameters and the transformation function, which are characterized by conditional moment inequalities involving U-statistics. We provide inference methods for them by constructing an inference approach for conditional moment inequality models in which the sample analogs of moments are U-statistics. We apply the proposed inference methods to evaluate the effect of heart transplants on patients' survival time using data from the Stanford Heart Transplant Study.
The effects of incentives, social norms, and employees' values on work performance
Authors: Michael Roos, Jessica Reale, Frederik Banning
Date: 2021-07-02 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Management

This agent-based model contributes to a theory of corporate culture in which company performance and employees' behaviour result from the interaction between financial incentives, motivational factors and endogenous social norms. Employees' personal values are the main drivers of behaviour. They shape agents' decisions about how much of their working time to devote to individual tasks, cooperative, and shirking activities. The model incorporates two aspects of the management style, analysed both in isolation and combination: (i) monitoring efforts affecting intrinsic motivation, i.e. the firm is either trusting or controlling, and (ii) remuneration schemes affecting extrinsic motivation, i.e. individual or group rewards. The simulations show that financial incentives can (i) lead to inefficient levels of cooperation, and (ii) reinforce value-driven behaviours, amplified by emergent social norms. The company achieves the highest output with a flat wage and a trusting management. Employees that value self-direction highly are pivotal, since they are strongly (de-)motivated by the management style.
Habits and demand changes after COVID-19
Authors: Mauro Bambi, Daria Ghilli, Fausto Gozzi, Marta Leocata
Date: 2021-07-02 00:00:00

In this paper, we investigate how the COVID-19 pandemics and more precisely the lockdown of a sector of the economy may have changed our habits and, there-fore, altered the demand of some goods even after the re-opening. In a two-sector infinite horizon economy, we show that the demand of the goods produced by the sector closed during the lockdown could shrink or expand with respect to their pre-pandemic level depending on the length of the lockdown and the relative strength of the satiation effect and the substitutability effect. We also provide conditions under which this sector could remain inactive even after the lockdown as well as an insight on the policy which should be adopted to avoid this outcome.
NB-IoT via LEO satellites: An efficient resource allocation strategy for uplink data transmission
Authors: O. Kodheli, N. Maturo, S. Chatzinotas, S. Andrenacci, F. Zimmer
Date: 2021-07-02 00:00:00
Tasks: Scheduling

In this paper, we focus on the use of Low-Eart Orbit (LEO) satellites providing the Narrowband Internet of Things (NB-IoT) connectivity to the on-ground user equipment (UEs). Conventional resource allocation algorithms for the NBIoT systems are particularly designed for terrestrial infrastructures, where devices are under the coverage of a specific base station and the whole system varies very slowly in time. The existing methods in the literature cannot be applied over LEO satellite-based NB-IoT systems for several reasons. First, with the movement of the LEO satellite, the corresponding channel parameters for each user will quickly change over time. Delaying the scheduling of a certain user would result in a resource allocation based on outdated parameters. Second, the differential Doppler shift, which is a typical impairment in communications over LEO, directly depends on the relative distance among users. Scheduling at the same radio frame users that overcome a certain distance would violate the differential Doppler limit supported by the NB-IoT standard. Third, the propagation delay over a LEO satellite channel is around 4-16 times higher compared to a terrestrial system, imposing the need for message exchange minimization between the users and the base station. In this work, we propose a novel uplink resource allocation strategy that jointly incorporates the new design considerations previously mentioned together with the distinct channel conditions, satellite coverage times and data demands of various users on Earth. The novel methodology proposed in this paper can act as a framework for future works in the field.
Dirichlet process approach for radio-based simultaneous localization and mapping
Authors: Jaebok Lee, Hyowon Kim, Henk Wymeersch, Sunwoo Kim
Date: 2021-07-02 00:00:00
Tasks: Clustering, Simultaneous Localization and Mapping

Due to 5G millimeter wave (mmWave), spatial channel parameters are becoming highly resolvable, enabling accurate vehicle localization and mapping. We propose a novel method of radio simultaneous localization and mapping (SLAM) with the Dirichlet process (DP). The DP, which can estimate the number of clusters as well as clustering, is capable of identifying the locations of reflectors by classifying signals when such 5G signals are reflected and received from various objects. We generate birth points using the measurements from 5G mmWave signals received by the vehicle and classify objects by clustering birth points generated over time. Each time we use the DP clustering method, we can map landmarks in the environment in challenging situations where false alarms exist in the measurements and change the cardinality of received signals. Simulation results demonstrate the performance of the proposed scheme. By comparing the results with the SLAM based on the Rao-Blackwellized probability hypothesis density filter, we confirm a slight drop in SLAM performance, but as a result, we validate that it has a significant gain in computational complexity.
Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors
Authors: Sung Joon Maeng, Md Moin Uddin Chowdhury, İsmail Güvenç, Arupjyoti Bhuyan, Huaiyu Dai
Date: 2021-07-02 00:00:00

The concept of drone corridors is recently getting more attention to enable connected, safe, and secure flight zones in the national airspace. To support beyond visual line of sight (BVLOS) operations of aerial vehicles in a drone corridor, cellular base stations (BSs) serve as a convenient infrastructure, since such BSs are widely deployed to provide seamless wireless coverage. However, antennas in the existing cellular networks are down-tilted to optimally serve their ground users, which results in coverage holes if they are also used to serve drones. In this letter, we consider the use of additional uptilted antennas at cellular BSs and optimize the uptilt angle to minimize outage probability for a given drone corridor. Our numerical results show how the beamwidth and the maximum drone corridor height affect the optimal value of the antenna uptilt angle.
Precoder Design for Physical-Layer Security and Authentication in Massive MIMO UAV Communications
Authors: Sung Joon Maeng, Yavuz Yapıcı, İsmail Güvenç, Arupjyoti Bhuyan, Huaiyu Dai
Date: 2021-07-02 00:00:00
Tasks: TAG

Supporting reliable and seamless wireless connectivity for unmanned aerial vehicles (UAVs) has recently become a critical requirement to enable various different use cases of UAVs. Due to their widespread deployment footprint, cellular networks can support beyond visual line of sight (BVLOS) communications for UAVs. In this paper, we consider cellular connected UAVs (C-UAVs) that are served by massive multiple-input-multiple-output (MIMO) links to extend coverage range, while also improving physical layer security and authentication. We consider Rician channel and propose a novel linear precoder design for transmitting data and artificial noise (AN). We derive the closed-form expression of the ergodic secrecy rate of C-UAVs for both conventional and proposed precoder designs. In addition, we obtain the optimal power splitting factor that divides the power between data and AN by asymptotic analysis. Then, we apply the proposed precoder design in the fingerprint embedding authentication framework, where the goal is to minimize the probability of detection of the authentication tag at an eavesdropper. In simulation results, we show the superiority of the proposed precoder in both secrecy rate and the authentication probability considering moderate and large number of antenna massive MIMO scenarios.
Geometric Machine Learning for Channel Covariance Estimation in Vehicular Networks
Authors: Imtiaz Nasim, Ahmed S. Ibrahim
Date: 2021-07-01 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Learning the covariance matrices of spatially-correlated wireless channels, in millimeter-wave (mmWave) vehicular communication, can be utilized in designing environmen-taware beamforming codebooks. Such channel covariance matrices can be represented on non-Euclidean Riemannian manifolds, thanks to their symmetric positive definite (SPD) characteristics. Consequently in this paper, we propose a Riemannian-Geometric machine learning (G-ML) approach for estimating the channel covariance matrices based on unsupervised K-Means model. The proposed K-means algorithm utilizes Log-Euclidean metric (LEM) as the distance measure among channel covariance matrices over the Riemannian manifolds. We show that our proposed K-Means G-ML model can achieve up to 80% less error compared to Euclidean-based K-Means algorithm, which applies clustering on the channel vectors themselves.
Precise Feature Selection and Case Study of Intrusion Detection in an Industrial Control System (ICS) Environment
Authors: Terry Guo, Animesh Dahal, Ambareen Siraj
Date: 2021-07-01 00:00:00
Tasks: feature selection, Intrusion Detection

This paper presents analytical techniques to improve redundancy and relevance assessment for precise selection of features in practical multi-class raw datasets. We propose a matrix-rank based $k$-medoids algorithm that guarantees to output all independent medoids. The new algorithm uses matrix rank as a robust indicator, while a traditional $k$-medoids algorithm depends on specific datasets and how the distance between any of two features is defined. Another advantage is that the total number of operations in the nested loops is bounded, different from some $k$-medoids algorithms that involve random search. Sparse regression is an efficient tool for feature relevance analysis, but its outcome can depend on what labeled datasets are employed. A compensation method is introduced in this paper to handle the unequality of class-occurrence in a practical raw dataset. To assess the proposed techniques quantitatively, an existing Industrial Control System (ICS) dataset is used to perform intrusion detection. The numerical results generated from this case study validate the effectiveness and necessity of the proposed analytical framework.
Phylogenetic Diversity Rankings in the Face of Extinctions: the Robustness of the Fair Proportion Index
Authors: Mareike Fischer, Andrew Francis, Kristina Wicke
Date: 2021-07-01 00:00:00
Tasks: Diversity

Planning for the protection of species often involves difficult choices about which species to prioritize, given constrained resources. One way of prioritizing species is to consider their "evolutionary distinctiveness", i.e. their relative evolutionary isolation on a phylogenetic tree. Several evolutionary isolation metrics or phylogenetic diversity indices have been introduced in the literature, among them the so-called Fair Proportion index (also known as the "evolutionary distinctiveness" score). This index apportions the total diversity of a tree among all leaves, thereby providing a simple prioritization criterion for conservation. Here, we focus on the prioritization order obtained from the Fair Proportion index and analyze the effects of species extinction on this ranking. More precisely, we analyze the extent to which the ranking order may change when some species go extinct and the Fair Proportion index is re-computed for the remaining taxa. We show that for each phylogenetic tree, there are edge lengths such that the extinction of one leaf per cherry completely reverses the ranking. Moreover, we show that even if only the lowest ranked species goes extinct, the ranking order may drastically change. We end by analyzing the effects of these two extinction scenarios (extinction of the lowest ranked species and extinction of one leaf per cherry) for a collection of empirical and simulated trees. In both cases, we can observe significant changes in the prioritization orders, highlighting the empirical relevance of our theoretical findings.
Epstein-Zin Utility Maximization on a Random Horizon
Authors: Joshua Aurand, Yu-Jui Huang
Date: 2019-03-21 00:00:00

This paper solves the consumption-investment problem under Epstein-Zin preferences on a random horizon. In an incomplete market, we take the random horizon to be a stopping time adapted to the market filtration, generated by all observable, but not necessarily tradable, state processes. Contrary to prior studies, we do not impose any fixed upper bound for the random horizon, allowing for truly unbounded ones. Focusing on the empirically relevant case where the risk aversion and the elasticity of intertemporal substitution are both larger than one, we characterize the optimal consumption and investment strategies using backward stochastic differential equations with superlinear growth on unbounded random horizons. This characterization, compared with the classical fixed-horizon result, involves an additional stochastic process that serves to capture the randomness of the horizon. As demonstrated in two concrete examples, changing from a fixed horizon to a random one drastically alters the optimal strategies.
Signaling receptor localization maximizes cellular information acquisition in spatially-structured, natural environments
Authors: Zitong Jerry Wang, Matt Thomson
Date: 2021-07-02 00:00:00

Cells in natural environments like tissue or soil sense and respond to extracellular ligands with intricately structured and non-monotonic spatial distributions that are sculpted by processes such as fluid flow and substrate adhesion. Nevertheless, traditional approaches to studying cell sensing assume signals are either uniform or monotonic, neglecting spatial structures of natural environments. In this work, we show that spatial sensing and navigation can be optimized by adapting the spatial organization of signaling pathways to the spatial structure of the environment. By viewing cell surface receptors as a sensor network, we develop an information theoretic framework for computing the optimal spatial organization of a sensing system for a given spatial signaling environment. Applying the framework to simulated environments, we find that spatial receptor localization maximizes information acquisition in many natural contexts, including tissue and soil. Receptor localization extends naturally to produce a dynamic protocol for redistributing signaling receptors during cell navigation and can be implemented in a cell using a feedback scheme. In a simulated tissue environment, dynamic receptor localization boosts navigation efficiency by 30-fold. Broadly, our framework readily adapts to studying how the spatial organization of signaling components other than receptors can be modulated to improve cellular information processing.
A computational theory for the production of limb movements
Authors: Emmanuel Guigon
Date: 2021-07-02 00:00:00

Motor control is a fundamental process that underlies all voluntary behavioral responses. Several different theories based on different principles (task dynamics, equilibrium-point theory, passive-motion paradigm, active inference, optimal control) account for specific aspects of how actions are produced, but fail to provide a unified view on this problem. Here we propose a concise theory of motor control based on three principles: optimal feedback control, control with a receding time horizon, and task representation by a series of via-points updated at fixed frequency. By construction, the theory provides a suitable solution to the degrees-of-freedom problem, i.e. trajectory formation in the presence of redundancies and noise. We show through computer simulations that the theory also explains the production of discrete, continuous, rhythmic and temporally-constrained movements, and their parametric and statistical properties (scaling laws, power laws, speed/accuracy tradeoffs). The theory has no free parameters and only limited variations in its implementation details and in the nature of noise are necessary to guarantee its explanatory power.