pwc-archive/papers-with-abstracts huggingface dataset:
Corridor for new mobility Aachen-Düsseldorf: Methods and concepts of the research project ACCorD
Authors: Laurent Kloeker, Amarin Kloeker, Fabian Thomsen, Armin Erraji, Lutz Eckstein, Serge Lamberty, Adrian Fazekas, Eszter Kalló, Markus Oeser, Charlotte Fléchon, Jochen Lohmiller, Pascal Pfeiffer, Martin Sommer, Helen Winter
Date: 2021-07-13 00:00:00

With the Corridor for New Mobility Aachen - D\"usseldorf, an integrated development environment is created, incorporating existing test capabilities, to systematically test and validate automated vehicles in interaction with connected Intelligent Transport Systems Stations (ITS-Ss). This is achieved through a time- and cost-efficient toolchain and methodology, in which simulation, closed test sites as well as test fields in public transport are linked in the best possible way. By implementing a digital twin, the recorded traffic events can be visualized in real-time and driving functions can be tested in the simulation based on real data. In order to represent diverse traffic scenarios, the corridor contains a highway section, a rural area, and urban areas. First, this paper outlines the project goals before describing the individual project contents in more detail. These include the concepts of traffic detection, driving function development, digital twin development, and public involvement.
The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI
Authors: Susan von Struensee
Date: 2021-07-11 00:00:00
Tasks: Ethics

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
Internet-of-Things Devices and Assistive Technologies for Healthcare: Applications, Challenges, and Opportunities
Authors: Marc Jayson Baucas, Petros Spachos, Stefano Gregori
Date: 2021-07-11 00:00:00

Medical conditions and cases are growing at a rapid pace, where physical space is starting to be constrained. Hospitals and clinics no longer have the ability to accommodate large numbers of incoming patients. It is clear that the current state of the health industry needs to improve its valuable and limited resources. The evolution of the Internet of Things (IoT) devices along with assistive technologies can alleviate the problem in healthcare, by being a convenient and easy means of accessing healthcare services wirelessly. There is a plethora of IoT devices and potential applications that can take advantage of the unique characteristics that these technologies can offer. However, at the same time, these services pose novel challenges that need to be properly addressed. In this article, we review some popular categories of IoT-based applications for healthcare along with their devices. Then, we describe the challenges and discuss how research can properly address the open issues and improve the already existing implementations in healthcare. Further possible solutions are also discussed to show their potential in being viable solutions for future healthcare applications
A Decision Model for Decentralized Autonomous Organization Platform Selection: Three Industry Case Studies
Authors: Elena Baninemeh, Siamak Farshidi, Slinger Jansen
Date: 2021-07-07 00:00:00
Tasks: Decision Making

Decentralized autonomous organizations as a new form of online governance arecollections of smart contracts deployed on a blockchain platform that intercede groupsof people. A growing number of Decentralized Autonomous Organization Platforms,such as Aragon and Colony, have been introduced in the market to facilitate thedevelopment process of such organizations. Selecting the best fitting platform ischallenging for the organizations, as a significant number of decision criteria, such aspopularity, developer availability, governance issues, and consistent documentation ofsuch platforms, should be considered. Additionally, decision-makers at theorganizations are not experts in every domain, so they must continuously acquirevolatile knowledge regarding such platforms and keep themselves updated.Accordingly, a decision model is required to analyze the decision criteria usingsystematic identification and evaluation of potential alternative solutions for adevelopment project. We have developed a theoretical framework to assist softwareengineers with a set of Multi-Criteria Decision-Making problems in software production.This study presents a decision model as a Multi-Criteria Decision-Making problem forthe decentralized autonomous organization platform selection problem. Weconducted three industry case studies in the context of three decentralizedautonomous organizations to evaluate the effectiveness and efficiency of the decisionmodel in assisting decision-makers.
A Fair and Ethical Healthcare Artificial Intelligence System for Monitoring Driver Behavior and Preventing Road Accidents
Authors: Soraia Oueida, Soaad Hossain, Yehia Kotb, Syed Ishtiaque Ahmed
Date: 2021-06-16 00:00:00
Tasks: Decision Making, Ethics, Fairness

This paper presents a new approach to prevent transportation accidents and monitor driver's behavior using a healthcare AI system that incorporates fairness and ethics. Dangerous medical cases and unusual behavior of the driver are detected. Fairness algorithm is approached in order to improve decision-making and address ethical issues such as privacy issues, and to consider challenges that appear in the wild within AI in healthcare and driving. A healthcare professional will be alerted about any unusual activity, and the driver's location when necessary, is provided in order to enable the healthcare professional to immediately help to the unstable driver. Therefore, using the healthcare AI system allows for accidents to be predicted and thus prevented and lives may be saved based on the built-in AI system inside the vehicle which interacts with the ER system.
Heterogeneously-Distributed Joint Radar Communications: Bayesian Resource Allocation
Authors: Linlong Wu, Kumar Vijay Mishra, Bhavani Shankar M. R., Björn Ottersten
Date: 2021-07-29 00:00:00
Tasks: Joint Radar-Communication

Due to spectrum scarcity, the coexistence of radar and wireless communication has gained substantial research interest recently. Among many scenarios, the heterogeneouslydistributed joint radar-communication system is promising due to its flexibility and compatibility of existing architectures. In this paper, we focus on a heterogeneous radar and communication network (HRCN), which consists of various generic radars for multiple target tracking (MTT) and wireless communications for multiple users. We aim to improve the MTT performance and maintain good throughput levels for communication users by a well-designed resource allocation. The problem is formulated as a Bayesian Cram\'er-Rao bound (CRB) based minimization subjecting to resource budgets and throughput constraints. The formulated nonconvex problem is solved based on an alternating descent-ascent approach. Numerical results demonstrate the efficacy of the proposed allocation scheme for this heterogeneous network.
Downlink Analysis of LEO Multi-Beam Satellite Communication in Shadowed Rician Channels
Authors: Eunsun Kim, Ian P. Roberts, Peter A. Iannucci, Jeffrey G. Andrews
Date: 2021-07-29 00:00:00

The coming extension of cellular technology to base-stations in low-earth orbit (LEO) requires a fresh look at terrestrial 3GPP channel models. Relative to such models, sky-to-ground cellular channels will exhibit less diffraction, deeper shadowing, larger Doppler shifts, and possibly far stronger cross-cell interference: consequences of high elevation angles and extreme "sectorization" of LEO satellite transmissions into partially-overlapping spot beams. To permit forecasting of expected signal-to-noise ratio (SNR), interference-to-noise ratio (INR) and probability of outage, we characterize the powers of desired and interference signals as received by ground users from such a LEO satellite. In particular, building on the Shadowed Rician channel model, we observe that co-cell and cross-cell sky-to-ground signals travel along similar paths, whereas terrestrial co- and cross-cell signals travel along very different paths. We characterize SNR, signal-to-interference ratio (SIR), and INR using transmit beam profiles and linear relationships that we establish between certain Shadowed Rician random variables. These tools allow us to simplify certain density functions and moments, facilitating future analysis. Numerical results yield insight into the key question of whether emerging LEO systems should be viewed as interference- or noise-limited.
Fast Beam Training for RIS-Assisted Uplink Communication
Authors: Chandradeep Singh, Kamal Singh, K. H. Liu
Date: 2021-07-23 00:00:00

In this work, we propose a beam training codebook for Reconfigurable Intelligent Surface (RIS) assisted mmWave uplink communication. Beam training procedure is important to establish a reliable link between user node and Access point (AP). A codebook based training procedure reduces the search time to obtain best possible phase shift by RIS controller to align incident beam at RIS in the direction of receiving node. We consider a semi passive RIS to assist RIS controller with a feedback of minimum overhead. It is shown that the procedure detects a mobile node with high probability in a short interval of time. Further we use the same codebook at user node to know the desired direction of communication via RIS.
Don't Separate, Learn to Remix: End-to-End Neural Remixing with Joint Optimization
Authors: Haici Yang, Shivani Firodiya, Nicholas J. Bryan, Minje Kim
Date: 2021-07-28 00:00:00
Tasks: Data Augmentation

The task of manipulating the level and/or effects of individual instruments to recompose a mixture of recordings, or remixing, is common across a variety of applications such as music production, audio-visual post-production, podcasts, and more. This process, however, traditionally requires access to individual source recordings, restricting the creative process. To work around this, source separation algorithms can separate a mixture into its respective components. Then, a user can adjust their levels and mix them back together. This two-step approach, however, still suffers from audible artifacts and motivates further work. In this work, we learn to remix music directly by re-purposing Conv-TasNet, a well-known source separation model, into two neural remixing architectures. To do this, we use an explicit loss term that directly measures remix quality and jointly optimize it with a separation loss. We evaluate our methods using the Slakh and MUSDB18 datasets and report remixing performance as well as the impact on source separation as a byproduct. Our results suggest that learning-to-remix significantly outperforms a strong separation baseline and is particularly useful for small volume changes.
Controlling epidemics through optimal allocation of test kits and vaccine doses across networks
Authors: Mingtao Xia, Lucas Böttcher, Tom Chou
Date: 2021-07-29 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning (RL)

Efficient testing and vaccination protocols are critical aspects of epidemic management. To study the optimal allocation of limited testing and vaccination resources in a heterogeneous contact network of interacting susceptible, recovered, and infected individuals, we present a degree-based testing and vaccination model for which we use control-theoretic methods to derive optimal testing and vaccination policies. Within our framework, we find that optimal intervention policies first target high-degree nodes before shifting to lower-degree nodes in a time-dependent manner. Using such optimal policies, it is possible to delay outbreaks and reduce incidence rates to a greater extent than uniform and reinforcement-learning-based interventions, particularly on certain scale-free networks.
Security of Distributed Parameter Cyber-Physical Systems: Cyber-Attack Detection in Linear Parabolic PDEs
Authors: Tanushree Roy, Satadru Dey
Date: 2021-07-29 00:00:00
Tasks: Cyber Attack Detection

Security of Distributed Parameter Cyber-Physical Systems (DPCPSs) is of critical importance in the face of cyber-attack threats. Although security aspects of Cyber-Physical Systems (CPSs) modelled by Ordinary differential Equations (ODEs) have been extensively explored during the past decade, security of DPCPSs has not received its due attention despite its safety-critical nature. In this work, we explore the security aspects of DPCPSs from a system theoretic viewpoint. Specifically, we focus on DPCPSs modelled by linear parabolic Partial Differential Equations (PDEs) subject to cyber-attacks in actuation channel. First, we explore the detectability of such attacks and derive conditions for stealthy attacks. Next, we develop a design framework for cyber-attack detection algorithms based on output injection observers. Such attack detection algorithms explicitly consider stability, robustness and attack sensitivity in their design. Finally, theoretical analysis and simulation studies are performed to illustrate the effectiveness of the proposed approach.
Classical Risk-Averse Control for a Finite-Horizon Borel Model
Authors: Margaret P. Chapman, Kevin M. Smith
Date: 2021-07-29 00:00:00

We study a risk-averse optimal control problem for a finite-horizon Borel model, where a cumulative cost is assessed via exponential utility. The setting permits non-linear dynamics, non-quadratic costs, and continuous state and control spaces but is less general than the problem of optimizing an expected utility. Our contribution is to show the existence of an optimal risk-averse controller without using state space augmentation and therefore offer a simpler solution method from first principles compared to what is currently available in the literature.
Frequency dynamics of the Northern European AC/DC power system: a look-ahead study
Authors: Matas Dijokas, Danilo Obradovic, Georgios Misyris, Tilman Weckesser, Thierry Van Cutsem
Date: 2021-07-29 00:00:00

A large share of renewable energy sources integrated into the national grids and an increased interconnection capacity with asynchronous networks, are the main contributors in reducing the kinetic energy storage in the Nordic Power System. Challenges arise to operate system after a loss of generation and to minimize the offshore systems interaction with onshore grids. To assess the associated challenges, a novel dynamic model was developed under the phasor approximation to represent the future Northern European Power System, including High Voltage Direct Current (HVDC) links and future offshore energy islands in the North Sea. First, the future frequency response is provided for two large disturbances to highlight the benefit of the developed model and point out potential future frequency issues. Consequently, further actions are investigated to better utilize the existing frequency containment reserves or to partially replace them using emergency droop-based power control of HVDC links. Lastly, the offshore grid interaction and frequency support to the Nordic network is investigated, and the dynamic response is compared for zero- and low-inertia designs of the offshore energy islands. The simulations were performed using industrial software, and the associated material is made publicly available.
Adaptive Sampling of Dynamic Systems for Generation of Fast and Accurate Surrogate Models
Authors: Torben Talis, Joris Weigert, Erik Esche, Jens-Uwe Repke
Date: 2021-07-29 00:00:00
Tasks: Model Predictive Control

For economic nonlinear model predictive control and dynamic real-time optimization fast and accurate models are necessary. Consequently, the use of dynamic surrogate models to mimic complex rigorous models is increasingly coming into focus. For dynamic systems, the focus so far had been on identifying a system's behavior surrounding a steady-state operation point. In this contribution, we propose a novel methodology to adaptively sample rigorous dynamic process models to generate a dataset for building dynamic surrogate models. The goal of the developed algorithm is to cover an as large as possible area of the feasible region of the original model. To demonstrate the performance of the presented framework it is applied on a dynamic model of a chlor-alkali electrolysis.
A Novel Passivity-Based Trajectory Tracking Control For Conservative Mechanical Systems
Authors: Robert Mahony
Date: 2021-07-29 00:00:00

Most passivity based trajectory tracking algorithms for mechanical systems can only stabilise reference trajectories that have constant energy. This paper overcomes this limitation by deriving a single variable Hamiltonian model for the reference trajectory and solving along the constrained trajectory to obtain a reference potential. This potential is then used as the model to shape the energy of the true system such that its free solutions include the desired reference trajectory. The proposed trajectory tracking algorithm interconnects the reference and true systems through a virtual spring damper along with an outer-loop energy pump/damper that stabilises the desired energy level of the interconnected system, ensuring asymptotic tracking of the desired trajectory. The resulting algorithm is a fully energy based trajectory tracking control for non-stationary trajectories of conservative mechanical systems.
Investigating molecular crowding during cell division in budding yeast with FRET
Authors: Sarah Lecinski, Jack W Shepherd, Lewis Frame, Imogen Hayton, Chris MacDonald, Mark C Leake
Date: 2021-07-29 00:00:00

Cell division, aging, and stress recovery triggers spatial reorganization of cellular components in the cytoplasm, including membrane bound organelles, with molecular changes in their compositions and structures. However, it is not clear how these events are coordinated and how they integrate with regulation of molecular crowding. We use the budding yeast Saccharomyces cerevisiae as a model system to study these questions using recent progress in optical fluorescence microscopy and crowding sensing probe technology. We used a F\"{o}rster Resonance Energy Transfer (FRET) based sensor, illuminated by confocal microscopy for high throughput analyses and Slimfield microscopy for single-molecule resolution, to quantify molecular crowding. We determine crowding in response to cellular growth of both mother and daughter cells, in addition to osmotic stress, and reveal hot spots of crowding across the bud neck in the burgeoning daughter cell. This crowding might be rationalized by the packing of inherited material, like the vacuole, from mother cells. We discuss recent advances in understanding the role of crowding in cellular regulation and key current challenges and conclude by presenting our recent advances in optimizing FRET-based measurements of crowding whilst simultaneously imaging a third color, which can be used as a marker that labels organelle membranes. Our approaches can be combined with synchronised cell populations to increase experimental throughput and correlate molecular crowding information with different stages in the cell cycle.
Estimation of functional diversity and species traits from ecological monitoring data
Authors: Alexey Ryabov, Bernd Blasius, Helmut Hillebrand, Irina Olenina, Thilo Gross
Date: 2021-07-29 00:00:00
Tasks: Diversity

The twin crises of climate change and biodiversity loss define a strong need for functional diversity monitoring. While the availability of high-quality ecological monitoring data is increasing, the quantification of functional diversity so far requires the identification of species traits, for which data is harder to obtain. However, the traits that are relevant for the ecological function of a species also shape its performance in the environment and hence should be reflected indirectly in its spatio-temporal distribution. Thus it may be possible to reconstruct these traits from a sufficiently extensive monitoring dataset. Here we use diffusion maps, a deterministic and de-facto parameter-free analysis method, to reconstruct a proxy representation of the species' traits directly from monitoring data and use it to estimate functional diversity. We demonstrate this approach both with simulated data and real-world phytoplankton monitoring data from the Baltic sea. We anticipate that wider application of this approach to existing data could greatly advance the analysis of changes in functional biodiversity.
Day-to-day and seasonal regularity of network passenger delay for metro networks
Authors: Panchamy Krishnakumari, Oded Cats, Hans van Lint
Date: 2021-07-07 00:00:00
Tasks: Clustering

In an effort to improve user satisfaction and transit image, transit service providers worldwide offer delay compensations. Smart card data enables the estimation of passenger delays throughout the network and aid in monitoring service performance. Notwithstanding, in order to prioritize measures for improving service reliability and hence reducing passenger delays, it is paramount to identify the system components - stations and track segments - where most passenger delay occurs. To this end, we propose a novel method for estimating network passenger delay from individual trajectories. We decompose the delay along a passenger trajectory into its corresponding track segment delay, initial waiting time and transfer delay. We distinguish between two different types of passenger delay in relation to the public transit network: average passenger delay and total passenger delay. We employ temporal clustering on these two quantities to reveal daily and seasonal regularity in delay patterns of the transit network. The estimation and clustering methods are demonstrated on one year of data from Washington metro network. The data consists of schedule information and smart card data which includes passenger-train assignment of the metro network for the months of August 2017 to August 2018. Our findings show that the average passenger delay is relatively stable throughout the day. The temporal clustering reveals pronounced and recurrent and thus predictable daily and weekly patterns with distinct characteristics for certain months.