pwc-archive/papers-with-abstracts huggingface dataset:
Vorstellung eines sozioinformatischen Analyseansatzes zur Technikfolgenabschätzung in Anlehnung an Vesters Sensitivitätsmodell am Beispiel des Unternehmens Uber als sozio-technisches System
Authors: Tobias D. Krafft
Date: 2021-03-26 00:00:00

What fits the human brain does not necessarily mean whatever is invented is meant for human brain. This is exactly what is implied by the definition of algorithm: \textit{Reducing and Simplifying}. Our cognitive apparatus, although endowed with certain parameters, ceases to comprehend chains of mathematical and logical steps used for algorithmic or mathematical purposes. Such a cognitive limit allows us to pay attention to the symbolic regularities which are meaningful. The more meaningless these symbols become, the fuzzier is our mental representation for it. Needless to say, that we actually tend to interact with activities that are meaningful, and discard those that are meaningless. By activities we mean steps, steps that form an algorithm. Could it be just an assumption? Are we really aversing from algorithms in modern day life? -- Ziel dieser Arbeit war eine kritische Auseinandersetzung mit dem biokybernetischen Denkansatz anhand des Sensitivit\"atsmodells von Frederic Vester, das in die sozioinformatische Analyse eines sozio-technischen Systems integriert und hinsichtlich der Fragestellung gepr\"uft wurde, inwieweit sich dieses Konzept zur Beurteilung von Planungsvorhaben bei sozio-technischen Systemen eignet. Dazu wurden F. Vesters Biokybernetischenn Grundregeln zur Feststellung der \"Uberlebensf\"ahigkeit eines Systems, auf ihre Anwendbarkeit in Bezug auf sozio-technische Systeme untersucht. Die Funktionalit\"at der Analysemethode wurde auf das nach der Definition von Andrea Kienle und Gabriele Kunau als sozio-technisches System charakterisierte Unternehmen Uber angewendet. Im Gegensatz zu unsystemischen Analysen besteht der Vorteil des Sensitivit\"atsmodells in der Vermittlung von Erkenntnissen \"uber innere Systemzusammenh\"ange. Abschlie{\ss}end wurde der vorgestellte Analyseansatz als transdisziplin\"are Weiterentwicklung des Sensitivit\"atsmodells charakterisiert.
LRez: C++ API and toolkit for analyzing and managing Linked-Reads data
Authors: Pierre Morisse, Claire Lemaitre, Fabrice Legeai
Date: 2021-03-26 00:00:00
Tasks: Management, TAG

Linked-Reads technologies, such as 10x Genomics, combine both the high-quality and low cost of short-reads sequencing and a long-range information, through the use of barcodes able to tag reads which originate from a common long DNA fragment. This technology has been employed in a broad range of applications including assembly or phasing of genomes, and structural variant calling. However, to date, no tool or API dedicated to the manipulation of Linked-Reads data exist. We introduce LRez, a C++ API and toolkit which allows easy management of Linked-Reads data. LRez includes various functionalities, for computing number of common barcodes between genomic regions, extracting barcodes from BAM files, as well as indexing and querying both BAM and FASTQ files to quickly fetch reads or alignments sharing one or multiple barcodes. LRez can thus be used in a broad range of applications requiring barcode processing, in order to improve their performances. LRez is implemented in C++, supported on Linux platforms, and available under AGPL-3.0 License at https://github.com/morispi/LRez.
Towards blind user's indoor navigation: a comparative study of beacons and decawave for indoor accurate location
Authors: Prabin Sharma, Sambad Bidari, Kisan Thapa, Antonio Valente, Hugo Paredes
Date: 2019-12-02 00:00:00
Tasks: Indoor Localization

There are many systems for indoor navigation specially built for visually impaired people but only some has good accuracy for navigation. While there are solutions like global navigation satellite systems for the localization outdoors, problems arise in urban scenarios and indoors due to insufficient or failed signal reception. To build a support system for navigation for visually impaired people, in this paper we present a comparison of indoor localization and navigation system, which performs continuous and real-time processing using commercially available systems (Beacons and Decawave) under the same experimental condition for the performance analysis. Error is calculated and analyzed using Euclidean distance and standard deviation for both the cases. We used Navigine Platform for this navigation system which allows both Tri-lateration as well as Fingerprinting algorithms. For calculating location we have used the concept of Time of Arrival and time of difference of arrivals. Taking into concern about the blind people, location is important as well as accuracy is necessity because small measurement in the walk is important to them. With this concern, in this paper, we are showing the comparative study of beacons and Decawave. The study and the accuracy tests of those systems for the blind people/user's in navigating indoor are presented in this paper.
Guaranteed $\mathcal{H}_\infty$ performance analysis and controller synthesis for interconnected linear systems from noisy input-state data
Authors: Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof
Date: 2021-03-26 00:00:00

The increase in available data and complexity of dynamical systems has sparked the research on data-based system performance analysis and controller design. Recent approaches can guarantee performance and robust controller synthesis based on noisy input-state data of a single dynamical system. In this paper, we extend a recent data-based approach for guaranteed performance analysis to distributed analysis of interconnected linear systems. We present a new set of sufficient LMI conditions based on noisy input-state data that guarantees $\mathcal{H}_\infty$ performance and have a structure that lends itself well to distributed controller synthesis from data. Sufficient LMI conditions based on noisy data are provided for the existence of a dynamic distributed controller that achieves $\mathcal{H}_\infty$ performance. The presented approach enables scalable analysis and control of large-scale interconnected systems from noisy input-state data sets.
Algorithm To Calculate Pulse from PPG Signal After Eliminating Touch Errors from the Fingertip Video Captured by Smartphone Camera
Authors: Ayan Chatterjee, Sundar Gopalakrishnan, Martin Gerdes, Santiago Martinez, Nibedita Pahari, Pankaj Khatiwada
Date: 2020-11-30 00:00:00

With the ongoing heart problems of the population worldwide, the medical requirements of the people are expected to increase. Electrocardiogram (ECG) is one of the proven to capture the heart response signal to assess the electrical and muscular functions of the heart. The ECG setup is expensive and needs proper training, and of course, it is not instant. For fast, accurate heart parameter monitoring, scientists pay attention to the photoplethysmogram signal (PPG), based on the light intensity of a particular wavelength. Android smartphone with a good quality camera has come to ordinary people's reach and has become one of the most necessary and rugged devices for today and future generations. We can use its powerful features to solve or assess heart state monitoring by capturing the image's necessary data. The mobile camera has a photo emitting diode and a photodetector. The light source illuminates the tissue. The photodetector calculates the small variation in light intensity associated with blood volume change in the vessels (mainly fingertips, toes, and ears). We have captured unfocused contact video to capture PPG using an Android Smartphone. Then, we removed a certain percent of camera touch errors based on average pixel intensity count in the red plane, and it is a new approach that has been introduced in this research. We used a 2nd order Butterworth (IIR) band pass filter for noise removal, FFT Hann Window for frequency analysis and leakage reduction. We have developed an algorithm using MATLAB as a development platform, for accurate pulse (BPM) measurement. Moreover, we have done a comparative analysis of developed algorithm with other available algorithms for PPG-based pulse calculation. In this study, the fingertip video was captured when the body was at rest
Emergence of more contagious COVID-19 variants from the coevolution of viruses and policy interventions
Authors: Aymeric Vie
Date: 2021-03-26 00:00:00

At the end of 2020, policy responses to the SARS-CoV-2 outbreak have been shaken by the emergence of virus variants. The emergence of these more contagious, more severe, or even vaccine-resistant strains have challenged worldwide policy interventions. Anticipating the emergence of these mutations to plan ahead adequate policies, and understanding how human behaviors may affect the evolution of viruses by coevolution, are key challenges. In this article, we propose coevolution with genetic algorithms (GAs) as a credible approach to model this relationship, highlighting its implications, potential and challenges. We present a dual GA model in which both viruses aiming for survival and policy measures aiming at minimising infection rates in the population, competitively evolve. Simulation runs reproduce the emergence of more contagious variants, and identifies the evolution of policy responses as a determinant cause of this phenomenon. This coevolution opens new possibilities to visualise the impact of governments interventions not only on outbreak dynamics, but also on its evolution, to improve the efficacy of policies.
Improved stability conditions for systems under aperiodic sampling: model- and data-based analysis
Authors: Stefan Wildhagen, Julian Berberich, Matthias Hirche, Frank Allgöwer
Date: 2021-03-26 00:00:00

Discrete-time systems under aperiodic sampling may serve as a modeling abstraction for a multitude of problems arising in cyber-physical and networked control systems. Recently, model- and data-based stability conditions for such systems were obtained by rewriting them as an interconnection of a linear time-invariant system and a delay operator, and subsequently, performing a robust stability analysis using a known bound on the gain of this operator. In this paper, we refine this approach: First, we show that the delay operator is input-feedforward passive and second, we compute its gain exactly. Based on these findings, we derive improved stability conditions both in case of full model knowledge and in case only data are available. In the latter, we require only a finite-length and potentially noisy state-input trajectory of the unknown system. In two examples, we illustrate the reduced conservativeness of the proposed stability conditions over existing ones.
A Natural Adaptive Process for Collective Decision-Making
Authors: Florian Brandl, Felix Brandt
Date: 2021-03-26 00:00:00
Tasks: Decision Making

Consider an urn filled with balls, each labeled with one of several possible collective decisions. Now, let a random voter draw two balls from the urn and pick her more preferred as the collective decision. Relabel the losing ball with the collective decision, put both balls back into the urn, and repeat. Once in a while, relabel a randomly drawn ball with a random collective decision. We prove that the empirical distribution of collective decisions produced by this process approximates a maximal lottery, a celebrated probabilistic voting rule proposed by Peter C. Fishburn (Rev. Econ. Stud., 51(4), 1984). In fact, the probability that the collective decision in round $n$ is made according to a maximal lottery increases exponentially in $n$. The proposed procedure is more flexible than traditional voting rules and bears strong similarities to natural processes studied in biology, physics, and chemistry as well as algorithms proposed in machine learning.
Data-Driven Control of Nonlinear Systems: Beyond Polynomial Dynamics
Authors: Robin Strässer, Julian Berberich, Frank Allgöwer
Date: 2020-11-23 00:00:00

In this paper, we present a data-driven controller design method for continuous-time nonlinear systems, using no model knowledge but only measured data affected by noise. While most existing approaches focus on systems with polynomial dynamics, our approach allows to design controllers for unknown systems with rational or general non-polynomial dynamics. We first derive a data-driven parametrization of unknown nonlinear systems with rational dynamics. By applying robust control techniques to this parametrization, we obtain sum-of-squares based criteria for designing controllers with closed-loop robust stability and performance guarantees for all systems which are consistent with the measured data and the assumed noise bound. We then apply this approach to control systems whose dynamics are linear in general non-polynomial basis functions by transforming them into polynomial systems. Finally, we apply the developed approaches to numerical examples.
Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy
Authors: Zhichao Liang, Zixiang Luo, Keyin Liu, Jingwei Qiu, Quanying Liu
Date: 2021-03-26 00:00:00
Tasks: Computational Efficiency, EEG, Electroencephalogram (EEG), Model Predictive Control, Seizure prediction

Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actuation strategies and lack the capability of self-adaptively adjusting stimulation inputs. In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic EEG with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies. The Koopman operator based linear dynamical model is embedded in the latent state space of the autoencoder neural network, in which we can approximate and update the Koopman operator online. The linear dynamical property of the Koopman operator ensures the convexity of the optimization problem for subsequent MPC control. The proposed deep Koopman operator model shows greater predictive capability than the baseline models (e.g., vector autoregressive model, kernel based method and recurrent neural network (RNN)) in both synthetic and real epileptic EEG data. Moreover, compared with the RNN-MPC framework, our Koopman-MPC framework can suppress seizure dynamics with better computational efficiency in both the Jansen-Rit model and the Epileptor model. Koopman-MPC framework opens a new window for model-based closed-loop neuromodulation and sheds light on nonlinear neurodynamics and feedback control policies.
Superiority of mild interventions against COVID-19 on public health and economic measures
Authors: Makoto Niwa, Yasushi Hara, Yusuke Matsuo, Hodaka Narita, Lim Yeongjoo, Shintaro Sengoku, Kota Kodama
Date: 2021-03-26 00:00:00

During the global spread of COVID-19, Japan has been among the top countries to maintain a relatively low number of infections, despite implementing limited institutional interventions. Using a Tokyo Metropolitan dataset, this study investigated how these limited intervention policies have affected public health and economic conditions in the COVID-19 context. A causal loop analysis suggested that there were risks to prematurely terminating such interventions. On the basis of this result and subsequent quantitative modelling, we found that the short-term effectiveness of a short-term pre-emptive stay-at-home request caused a resurgence in the number of positive cases, whereas an additional request provided a limited negative add-on effect for economic measures (e.g. the number of electronic word-of-mouth (eWOM) communications and restaurant visits). These findings suggest the superiority of a mild and continuous intervention as a long-term countermeasure under epidemic pressures when compared to strong intermittent interventions.
CNN-based Discriminative Training for Domain Compensation in Acoustic Event Detection with Frame-wise Classifier
Authors: Tiantian Tang, Xinyuan Zhou, Yanhua Long, Yijie Li, Jiaen Liang
Date: 2021-03-26 00:00:00
Tasks: Event Detection

Domain mismatch is a noteworthy issue in acoustic event detection tasks, as the target domain data is difficult to access in most real applications. In this study, we propose a novel CNN-based discriminative training framework as a domain compensation method to handle this issue. It uses a parallel CNN-based discriminator to learn a pair of high-level intermediate acoustic representations. Together with a binary discriminative loss, the discriminators are forced to maximally exploit the discrimination of heterogeneous acoustic information in each audio clip with target events, which results in a robust paired representations that can well discriminate the target events and background/domain variations separately. Moreover, to better learn the transient characteristics of target events, a frame-wise classifier is designed to perform the final classification. In addition, a two-stage training with the CNN-based discriminator initialization is further proposed to enhance the system training. All experiments are performed on the DCASE 2018 Task3 datasets. Results show that our proposal significantly outperforms the official baseline on cross-domain conditions in AUC by relative $1.8-12.1$% without any performance degradation on in-domain evaluation conditions.
Boundary Control of Traffic Congestion Modeled as a Non-stationary Stochastic Process
Authors: Xun Liu, Hossein Rastgoftar
Date: 2021-03-26 00:00:00
Tasks: Management, Model Predictive Control

In this paper, we introduce a new conservation-based approach to model traffic dynamics, and apply the model predictive control (MPC) approach to control the boundary traffic inflow and outflow, so that the traffic congestion is reduced. We establish an interface between the Simulation of Urban Mobility (SUMO) software and MATLAB to define a network of interconnected roads (NOIR) as a directed graph, and present traffic congestion management as a network control problem. By formally specifying the traffic feasibility conditions, and using the linear temporal logic, we present the proposed MPC-based boundary control problem as a quadratic programming with linear equality and inequality constraints. The success of the proposed traffic boundary control is demonstrated by simulation of traffic congestion control in Center City Philadelphia.
RFI Mitigation for One-bit UWB Radar Systems
Authors: Tianyi Zhang, Jiaying Ren, Jian Li, Lam H. Nguyen, Petre Stoica
Date: 2021-02-17 00:00:00
Tasks: Computational Efficiency, Quantization

Radio frequency interference (RFI) mitigation is critical to the proper operation of ultra-wideband (UWB) radar systems since RFI can severely degrade the radar imaging capability and target detection performance. In this paper, we address the RFI mitigation problem for one-bit UWB radar systems. A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the Continuous Time Binary Value (CTBV) technology. This sampling strategy compares the signal to a known threshold varying with slow-time and therefore can be used to achieve a rather high sampling rate and quantization resolution with rather simple and affordable hardware. This paper establishes a proper data model for the RFI sources and proposes a novel RFI mitigation method for the one-bit UWB radar system that uses the CTBV sampling technique. Specifically, we first model the RFI sources as a sum of sinusoids with frequencies fixed during the coherent processing interval (CPI) and we exploit the sparsity of the RFI spectrum. We extend a majorization-minimization based 1bRELAX algorithm, referred to as 1bMMRELAX, to estimate the RFI source parameters from the signed measurements obtained by using the CTBV sampling strategy. We also devise a new fast frequency initialization method based on the Alternating Direction Method of Multipliers (ADMM) methodology for the extended 1bMMRELAX algorithm to significantly improve its computational efficiency. Moreover, an ADMM-based sparse method is introduced to recover the desired radar echoes using the estimated RFI parameters. Both simulated and experimental results are presented to demonstrate that our proposed algorithm outperforms the existing digital integration method, especially for severe RFI cases.
Thermal Fault Detection and Localization Framework for Large Format Batteries
Authors: Sara Sattarzadeh, Tanushree Roy, Satadru Dey
Date: 2021-03-26 00:00:00
Tasks: Fault Detection

Safety against thermal failures is crucial in battery systems. Real-time thermal diagnostics can be a key enabler of such safer batteries. Thermal fault diagnostics in large format pouch or prismatic cells pose additional challenges compared to cylindrical cells. These challenges arise from the fact that the temperature distribution in large format cells is at least two-dimensional in nature (along length and breadth) while such distribution can be reasonably approximated in one dimension (along radial direction) in cylindrical cells. This difference makes the placement of temperature sensor(s) non-trivial and the design of detection algorithm challenging. In this work, we address these issues by proposing a framework that (i) optimizes the sensor locations to improve detectability and isolability of thermal faults, and (ii) designs a filtering scheme for fault detection and localization based on a two-dimensional thermal model. The proposed framework is illustrated by experimental and simulation studies on a commercial battery cell.
Amnesty Policy and Elite Persistence in the Postbellum South: Evidence from a Regression Discontinuity Design
Authors: Jason Poulos
Date: 2021-03-26 00:00:00
Tasks: regression

This paper investigates the impact of Reconstruction-era amnesty policy on the officeholding and wealth of elites in the postbellum South. Amnesty policy restricted the political and economic rights of Southern elites for nearly three years during Reconstruction. I estimate the effect of being excluded from amnesty on elites' future wealth and political power using a regression discontinuity design that compares individuals just above and below a wealth threshold that determined exclusion from amnesty. Results on a sample of Reconstruction convention delegates show that exclusion from amnesty significantly decreased the likelihood of ex-post officeholding. I find no evidence that exclusion impacted later census wealth for Reconstruction delegates or for a larger sample of known slaveholders who lived in the South in 1860. These findings are in line with previous studies evidencing both changes to the identity of the political elite, and the continuity of economic mobility among the planter elite across the Civil War and Reconstruction.
A Hybrid Queuing Model for Coordinated Vehicle Platooning on Mixed-Autonomy Highways: Training and Validation
Authors: Haoran Su, Zhengjie Ji, Karl. H. Johansson, Li Jin
Date: 2021-03-26 00:00:00
Tasks: Autonomous Vehicles, Decision Making

Platooning of connected and autonomous vehicles (CAVs) is an emerging technology with a strong potential for throughput improvement and fuel reduction. Adequate macroscopic models are critical for system-level efficiency and reliability of platooning. In this paper, we consider a hybrid queuing model for a mixed-autonomy highway section and develop an easy-to-use training algorithm. The model predicts CAV and non-CAV counts according to the traffic demand as well as key parameters of the highway section. The training algorithm learns the highway parameters from observed data in real time. We test the model and the algorithm in Simulation of Urban Mobility (SUMO) and show that the prediction error is around 15% in a stationary setting and around 25% in a non-stationary setting. We also show that the trained model leads to a platoon headway regulation policy very close to the simulated optimum. The proposed model and algorithm can directly support model-predictive decision-making for platooning in mixed autonomy.