pwc-archive/papers-with-abstracts huggingface dataset:
Grid-Based Stochastic Model Predictive Control for Trajectory Planning in Uncertain Environments
Authors: Tim Brüdigam, Fulvio di Luzio, Lucia Pallottino, Dirk Wollherr, Marion Leibold
Date: 2020-07-24 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Model Predictive Control, Trajectory Planning

Stochastic Model Predictive Control has proved to be an efficient method to plan trajectories in uncertain environments, e.g., for autonomous vehicles. Chance constraints ensure that the probability of collision is bounded by a predefined risk parameter. However, considering chance constraints in an optimization problem can be challenging and computationally demanding. In this paper, we present a grid-based Stochastic Model Predictive Control approach. This approach allows to determine a simple deterministic reformulation of the chance constraints and reduces the computational effort, while considering the stochastic nature of the environment. Within the proposed method, we first divide the environment into a grid and, for each predicted step, assign each cell a probability value, which represents the probability that this cell will be occupied by surrounding vehicles. Then, the probabilistic grid is transformed into a binary grid of admissible and inadmissible cells by applying a threshold, representing a risk parameter. Only cells with an occupancy probability lower than the threshold are admissible for the controlled vehicle. Given the admissible cells, a convex hull is generated, which can then be used for trajectory planning. Simulations of an autonomous driving highway scenario show the benefits of the proposed grid-based Stochastic Model Predictive Control method.
Tile test for back-testing risk evaluation
Authors: Gilles Zumbach
Date: 2020-07-24 00:00:00

A new test for measuring the accuracy of financial market risk estimations is introduced. It is based on the probability integral transform (PIT) of the ex post realized returns using the ex ante probability distributions underlying the risk estimation. If the forecast is correct, the result of the PIT, that we called probtile, should be an iid random variable with a uniform distribution. The new test measures the variance of the number of probtiles in a tiling over the whole sample. Using different tilings allow to check the dynamic and the distributional aspect of risk methodologies. The new test is very powerful, and new benchmarks need to be introduced to take into account subtle mean reversion effects induced by some risk estimations. The test is applied on 2 data sets for risk horizons of 1 and 10 days. The results show unambiguously the importance of capturing correctly the dynamic of the financial market, and exclude some broadly used risk methodologies.
Robust Vision Using Retro Reflective Markers for Remote Handling in ITER
Date: 2020-07-27 00:00:00
Tasks: Depth Estimation, Stereo Depth Estimation

The International Thermonuclear Experimental Reactor (ITER)'s working
environment is characterized by extreme conditions, that deem maintenance and
inspection tasks to be carried out through remote handling. 3D Node is a
hardware/software module that extracts critical information from the remote
environment during fine alignment tasks using an eye-in-hand camera system and
updates the models behind the virtual reality-based remote handling platform.
In this work we develop a retro-reflective marker-based version of 3D Node that
estimates the pose of a planar target, the knuckle of the cassette locking
system, using the markers attached to its surface. We demonstrate a pin-tool
insertion task using these methods. Results show that our approach works
reliably with a single low-resolution camera and outperforms the previously
researched stereo depth estimation based approaches. We conclude that
retro-reflective marker-based tracking has the potential to be a key enabler
for remote handling operations in ITER.
Performance-Driven Cascade Controller Tuning with Bayesian Optimization
Date: 2020-07-24 00:00:00
Tasks: Bayesian Optimization

We propose a performance-based autotuning method for cascade control systems,
where the parameters of a linear axis drive motion controller from two control
loops are tuned jointly. Using Bayesian optimization as all parameters are
tuned simultaneously, the method is guaranteed to converge asymptotically to
the global optimum of the cost. The data-efficiency and performance of the
method are studied numerically for several training configurations and compared
numerically to those achieved with classical tuning methods and to the
exhaustive evaluation of the cost. On the real system, the tracking performance
and robustness against disturbances are compared experimentally to nominal
tuning. The numerical study and the experimental data both demonstrate that the
proposed automated tuning method is efficient in terms of required tuning
iterations, robust to disturbances, and results in improved tracking.
Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles
Authors: Teng Liu, Bo wang, Dongpu Cao, Xiaolin Tang, Yalian Yang
Date: 2020-07-24 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

To improve the driving mobility and energy efficiency of connected autonomous electrified vehicles, this paper presents an integrated longitudinal speed decision-making and energy efficiency control strategy. The proposed approach is a hierarchical control architecture, which is assumed to consist of higher-level and lower-level controls. As the core of this study, model predictive control and reinforcement learning are combined to improve the powertrain mobility and fuel economy for a group of automated vehicles. The higher-level exploits the signal phase and timing and state information of connected autonomous vehicles via vehicle to infrastructure and vehicle to vehicle communication to reduce stopping at red lights. The higher-level outputs the optimal vehicle velocity using model predictive control technique and receives the power split control from the lower-level con-troller. These two levels communicate with each other via a controller area network in the real vehicle. The lower-level utilizes a model-free reinforcement learning method to improve the fuel economy for each connected autonomous vehicle. Numerical tests illustrate that vehicle mobility can be noticeably improved (traveling time reduced by 30%) by reducing red-light idling. The effectiveness and performance of the proposed method are validated via comparison analysis among different energy efficiency controls (fuel economy promoted by 13%).
Combined Sparse Regularization for Nonlinear Adaptive Filters
Authors: Danilo Comminiello, Michele Scarpiniti, Simone Scardapane, Luis A. Azpicueta-Ruiz, Aurelio Uncini
Date: 2020-07-24 00:00:00

Nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity. Recently, a class of proportionate algorithms has been proposed for nonlinear filters to leverage sparsity of their coefficients. However, the choice of the norm penalty of the cost function may be not always appropriate depending on the problem. In this paper, we introduce an adaptive combined scheme based on a block-based approach involving two nonlinear filters with different regularization that allows to achieve always superior performance than individual rules. The proposed method is assessed in nonlinear system identification problems, showing its effectiveness in taking advantage of the online combined regularization.
An Extensive Survey on the Internet of Drones
Authors: Pietro Boccadoro, Domenico Striccoli, Luigi Alfredo Grieco
Date: 2020-07-24 00:00:00
Tasks: Survey

The Internet of Drones (IoD) recently gained momentum due to its high adaptability to a wide variety of complex scenarios. Indeed, Unmanned Aerial Vehicles (UAVs) can successfully be employed in different applications, thanks to some technological and practical advantages: high mobility, capability to extend wireless coverage areas, or ability to reach places inaccessible to humans. Moreover, the employment of drones promisingly improves the performance parameters of different network architectures. Nevertheless, the adoption of networks of drones gives rise to several issues related to the unreliability of the wireless medium, the duration of batteries, and the high mobility degree, which may cause frequent topology changes. Also security and privacy issues need to be properly investigated. With respect to other surveys on IoD-related topics, the goal of the present work is to categorize the multifaceted aspects of IoD, proposing a classification approach of the IoD environment that develops along two main directions. At a macroscopic level, it follows the structure of the Internet protocol stack, starting from the physical layer and extending to the upper layers, without neglecting cross-layer and optimization approaches. At a finer level, all the most relevant works belonging to each layer of the stack are further classified, according to the different issues peculiar of the layer, and highlighting the most relevant differences with the other surveys present in literature. To provide a deeper insight in the theme, the present work embraces many facets of the IoD, including privacy and security considerations as well as the potential economic impact of the IoD. Finally, a discussion on the main research challenges and possible future directions is carried out, focusing on the open issues and the most promising technologies that deserve to be further developed in the IoD field.
Generalized likelihood ratio test detector for a modified replacement model target in a multivariate t-distributed background
Authors: James Theiler
Date: 2020-07-24 00:00:00

A closed-form expression is derived for the generalized likelihood ratio test (GLRT) detector of a subpixel target in a multispectral image whose area and brightness are both unknown. This expression extends a previous result (which assumed a Gaussian background distribution) to a fatter tailed elliptically-contoured (EC) multivariate t-distributed background. Numerical experiments with simulated data indicate that the EC-based detector outperforms the simpler Gaussian-based detectors, and that the relative performance of the new detector, compared to other EC-based detectors, depends on the regime of target strength and background occlusion.
Safe Model-Based Reinforcement Learning for Systems with Parametric Uncertainties
Authors: S M Nahid Mahmud, Scott A Nivison, Zachary I. Bell, Rushikesh Kamalapurkar
Date: 2020-07-24 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases. In general, safety guarantees are critical in reinforcement learning when the system is safety-critical and/or task restarts are not practically feasible. In optimal control theory, safety requirements are often expressed in terms of state and/or control constraints. In recent years, reinforcement learning approaches that rely on persistent excitation have been combined with a barrier transformation to learn the optimal control policies under state constraints. To soften the excitation requirements, model-based reinforcement learning methods that rely on exact model knowledge have also been integrated with the barrier transformation framework. The objective of this paper is to develop safe reinforcement learning method for deterministic nonlinear systems, with parametric uncertainties in the model, to learn approximate constrained optimal policies without relying on stringent excitation conditions. To that end, a model-based reinforcement learning technique that utilizes a novel filtered concurrent learning method, along with a barrier transformation, is developed in this paper to realize simultaneous learning of unknown model parameters and approximate optimal state-constrained control policies for safety-critical systems.
Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO
Authors: Mehmet Ali Aygul, Mahmoud Nazzal, Huseyin Arslan
Date: 2020-07-24 00:00:00
Tasks: Dictionary Learning

Millimeter-wave (mmWave) massive multiple-input-multiple-output (mMIMO) is reported as a key enabler in the fifth-generation communication and beyond. It is customary to use a lens antenna array to transform a mmWave mMIMO channel into a beamspace where the channel exhibits sparsity. This beamspace transformation is equivalent to performing a Fourier transformation of the channel. Still, a Fourier transformation is not necessarily the optimal one, due to many reasons. Accordingly, this paper proposes using a learned sparsifying dictionary as the transformation operator leading to another beamspace. Since the dictionary is obtained by training over actual channel measurements, this transformation is shown to yield two immediate advantages. First is enhancing channel sparsity, thereby leading to more efficient pilot reduction. Second is improving the channel representation quality, and thus reducing the underlying power leakage phenomenon. Consequently, this allows for both improved channel estimation and facilitated beam selection in mmWave mMIMO. Besides, a learned dictionary is also used as the precoding operator for the same reasons. Extensive simulations under various operating scenarios and environments validate the added benefits of using learned dictionaries in improving the channel estimation quality and the beam selectivity, thereby improving the spectral efficiency.
Casework applications of probabilistic genotyping methods for DNA mixtures that allow relationships between contributors
Authors: Peter J. Green, Julia Mortera, Lourdes Prieto
Date: 2020-07-24 00:00:00

In both criminal cases and civil cases there is an increasing demand for the analysis of DNA mixtures involving relationships. The goal might be, for example, to identify the contributors to a DNA mixture where the donors may be related, or to infer the relationship between individuals based on a DNA mixture. This paper applies a recent approach to modelling and computation for DNA mixtures involving contributors with arbitrarily complex relationships to two real cases from the Spanish Forensic Police.
Characterizing SARS-CoV-2 mutations in the United States
Authors: Rui Wang, Jiahui Chen, Kaifu Gao, Yuta Hozumi, Changchuan Yin, Guo-Wei Wei
Date: 2020-07-24 00:00:00
Tasks: Protein Folding

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been mutating since it was first sequenced in early January 2020. The genetic variants have developed into a few distinct clusters with different properties. Since the United States (US) has the highest number of viral infected patients globally, it is essential to understand the US SARS-CoV-2. Using genotyping, sequence-alignment, time-evolution, $k$-means clustering, protein-folding stability, algebraic topology, and network theory, we reveal that the US SARS-CoV-2 has four substrains and five top US SARS-CoV-2 mutations were first detected in China (2 cases), Singapore (2 cases), and the United Kingdom (1 case). The next three top US SARS-CoV-2 mutations were first detected in the US. These eight top mutations belong to two disconnected groups. The first group consisting of 5 concurrent mutations is prevailing, while the other group with three concurrent mutations gradually fades out. Our analysis suggests that female immune systems are more active than those of males in responding to SARS-CoV-2 infections. We identify that one of the top mutations, 27964C$>$T-(S24L) on ORF8, has an unusually strong gender dependence. Based on the analysis of all mutations on the spike protein, we further uncover that three of four US SASR-CoV-2 substrains become more infectious. Our study calls for effective viral control and containing strategies in the US.
Stochastic physics of species extinctions in a large population
Date: 2020-07-24 00:00:00

Species extinction is a core process that affects the diversity of life on
Earth. Competition between species in a population is considered by ecological
niche-based theories as a key factor leading to different severity of species
extinctions. There are population dynamics models that describe a simple and
easily understandable mechanism for resource competition. However, these models
can not efficiently characterize and quantify new emergent extinctions in a
large population appearing due to environmental forcing. To address this issue
we develop a stochastic physics-inspired approach to analyze how environmental
forcing influences the severity of species extinctions in such models. This
approach is based on the large deviations theory of stochastic processes (the
Freidlin-Wentzell theory). We show that there are three possible fundamentally
different scenarios of extinctions, which we call catastrophic extinctions,
asymmetric ones, and extinctions with exponentially small probabilities. The
realization of those scenarios depends on environmental noise properties and
the boundaries of niches, which define the domain, where species survive.
Furthermore, we describe a hysteresis effect in species extinction showing that
fluctuations can lead to dramatic consequences even if an averaged resource
supply is sufficient to support population survival. Our stochastic
physics-inspired approach generalizes niche theory by accounting environmental
forcing and will be useful to find, by available data, which environmental
perturbations may induce extinctions.
Pilot Low-Cost Concentrating Solar Power Systems Deployment in Sub-Saharan Africa: A Case Study of Implementation Challenges
Authors: Emmanuel Wendsongre Ramde, Eric Tutu Tchao, Yesuenyeagbe Atsu Kwabla Fiagbe, Jerry John Kponyo, Asakipaam Simon Atuah
Date: 2020-07-14 00:00:00

Electricity is one of the most crucial resources that drives any given nation's growth and development. The latest Sustainable Development Goals report indicates Africa still has a high deficit in electricity generation. Concentrating solar power seems to be a potential option to fill the deficit. That is because most of the components of concentrating solar power plants are readily available on the African market at affordable prices, and there are qualified local persons to build the plants. Pilot micro-concentrating solar power plants have been implemented in Sub-Saharan Africa and have shown promising results that could be expanded and leveraged for large-scale electricity generation. An assessment of a pilot concentrating solar power plant in the sub-region noticed one noteworthy obstacle that is the failure of the tracking system to reduce the operating energy cost of running the tracking control system and improve the multifaceted heliostat focusing behavior. This paper highlights the energy situation and the current development in concentrating solar power technology research in Africa. The paper also presents a comprehensive review of the state-of-the-art solar tracking systems for central receiver systems to illustrate the current direction of research regarding the design of low-cost tracking systems in terms of computational complexity, energy consumption, and heliostat alignment accuracy.
The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model
Authors: Peng-Fei Dai, Xiong Xiong, Wei-Xing Zhou
Date: 2020-07-25 00:00:00

This paper aims to examine whether the global economic policy uncertainty (GEPU) and uncertainty changes have different impacts on crude oil futures volatility. We establish single-factor and two-factor models under the GARCH-MIDAS framework to investigate the predictive power of GEPU and GEPU changes excluding and including realized volatility. The findings show that the models with rolling-window specification perform better than those with fixed-span specification. For single-factor models, the GEPU index and its changes, as well as realized volatility, are consistent effective factors in predicting the volatility of crude oil futures. Specially, GEPU changes have stronger predictive power than the GEPU index. For two-factor models, GEPU is not an effective forecast factor for the volatility of WTI crude oil futures or Brent crude oil futures. The two-factor model with GEPU changes contains more information and exhibits stronger forecasting ability for crude oil futures market volatility than the single-factor models. The GEPU changes are indeed the main source of long-term volatility of the crude oil futures.
Preliminary Assessment of hands motor imagery in theta- and beta-bands for Brain-Machine-Interfaces using functional connectivity analysis
Authors: Jorge Antonio Gaxiola Tirado
Date: 2020-07-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG), feature selection, Functional Connectivity, Motor Imagery

The use of time- and frequency-based features has proven effective in the process of classifying mental tasks in Brain Computer Interfaces (BCIs). Still, most of those methods provide little insight about the underlying brain activity and functions. Thus, a better understanding of the mechanisms and dynamics of brain activity, is necessary in order to obtain useful and informative features for BCIs. In the present study, the objective is to investigate the differences in functional connectivity of two motor imagery tasks, through a partial directed coherence (PDC) analysis, which is a frequency-domain metric that provides information about directionality in the interaction between signals recorded at different channels. Four healthy subjects participated in this study, two mental tasks were evaluated: Imagination of the movement of the right hand or left hand. We carry out the differentiation of these tasks through two different approaches: on one hand, the traditional one based on spectral power; on the other hand, an approach based on PDC. The results showed that EEG-based PDC analysis provides additional information and it can potentially improve the feature selection mainly in the beta frequency band.
Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements and Challenges
Authors: Praveen Kumar Reddy Maddikunta, Saqib Hakak, Mamoun Alazab, Sweta Bhattacharya, Thippa Reddy Gadekallu, Wazir Zada Khan, Quoc-Viet Pham
Date: 2020-07-25 00:00:00

In the next few years, smart farming will reach each and every nook of the world. The prospects of using unmanned aerial vehicles (UAV) for smart farming are immense. However, the cost and the ease in controlling UAVs for smart farming might play an important role for motivating farmers to use UAVs in farming. Mostly, UAVs are controlled by remote controllers using radio waves. There are several technologies such as WiFi or ZigBee that are also used for controlling UAVs. However, Smart Bluetooth (also referred to as Bluetooth Low Energy) is a wireless technology used to transfer data over short distances. Bluetooth smart is cheaper than other technologies and has the advantage of being available on every smart phone. Farmers can use any smart phone to operate their respective UAVs along with Bluetooth Smart enabled agricultural sensors in the future. However, certain requirements and challenges need to be addressed before UAVs can be operated for smart agriculture-related applications. Hence, in this article, an attempt has been made to explore the types of sensors suitable for smart farming, potential requirements and challenges for operating UAVs in smart agriculture. We have also identified the future applications of using UAVs in smart farming.