pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Adaptive Formation Control for Multi-UAV to Enable Connectivity
Authors: Rahim Rahmani, Ramin Firouzi, Theo Kanter
Date: 2020-08-17 00:00:00

There is increasing demand for control of multi-robot and as well distributing large amounts of content to cluster of Unmanned Aerial Vehicles (UAV) on the operation. In recent years several large-scale accidents have happened. To facilitate rescue operations and gather information, the technology that can access and map inaccessible areas is needed. This paper presents a disruptive approach to address the issues with communication, data collection and data sharing for UAV units in inaccessible or dead zones and We demonstrated feasibility of the approach and evaluate its advantages over the Ad Hoc architecture involving autonomous gateways
Analysing a built-in advantage in asymmetric darts contests using causal machine learning
Date: 2020-08-17 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

We analyse a sequential contest with two players in darts where one of the
contestants enjoys a technical advantage. Using methods from the causal machine
learning literature, we analyse the built-in advantage, which is the
first-mover having potentially more but never less moves. Our empirical
findings suggest that the first-mover has an 8.6 percentage points higher
probability to win the match induced by the technical advantage. Contestants
with low performance measures and little experience have the highest built-in
advantage. With regard to the fairness principle that contestants with equal
abilities should have equal winning probabilities, this contest is ex-ante fair
in the case of equal built-in advantages for both competitors and a randomized
starting right. Nevertheless, the contest design produces unequal probabilities
of winning for equally skilled contestants because of asymmetries in the
built-in advantage associated with social pressure for contestants competing at
home and away.
An epidemic model for an evolving pathogen with strain-dependent immunity
Date: 2020-08-17 00:00:00

Between pandemics, the influenza virus exhibits periods of incremental
evolution via a process known as antigenic drift. This process gives rise to a
sequence of strains of the pathogen that are continuously replaced by newer
strains, preventing a build up of immunity in the host population. In this
paper, a parsimonious epidemic model is defined that attempts to capture the
dynamics of evolving strains within a host population. The `evolving strains'
epidemic model has many properties that lie in-between the
Susceptible-Infected-Susceptible and the Susceptible-Infected-Removed epidemic
models, due to the fact that individuals can only be infected by each strain
once, but remain susceptible to reinfection by newly emerged strains. Coupling
results are used to identify key properties, such as the time to extinction. A
range of reproduction numbers are explored to characterize the model, including
a novel quasi-stationary reproduction number that can be used to describe the
re-emergence of the pathogen into a population with `average' levels of strain
immunity, analogous to the beginning of the winter peak in influenza. Finally
the quasi-stationary distribution of the evolving strains model is explored via
simulation.
Scoring the Terabit/s Goal:Broadband Connectivity in 6G
Authors: Nandana Rajatheva, Italo Atzeni, Simon Bicais, Emil Bjornson, Andre Bourdoux, Stefano Buzzi, Carmen D'Andrea, Jean-Baptiste Dore, Serhat Erkucuk, Manuel Fuentes, Ke Guan, Yuzhou Hu, Xiaojing Huang, Jari Hulkkonen, Josep Miquel Jornet, Marcos Katz, Behrooz Makki, Rickard Nilsson, Erdal Panayirci, Khaled Rabie, Nuwanthika Rajapaksha, MohammadJavad Salehi, Hadi Sarieddeen, Shahriar Shahabuddin, Tommy Svensson, Oskari Tervo, Antti Tolli, Qingqing Wu, Wen Xu
Date: 2020-08-17 00:00:00

This paper explores the road to vastly improving the broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, with peak data rates up to 1 Tbps. Several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be mainly used for pushing the peak rates of point-to-point links. Finally, at the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latency, higher reliability, and reduced complexity.
Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning
Authors: Robert Szczepanek
Date: 2020-10-05 00:00:00
Tasks: Pedestrian Detection

At the turn of February and March 2020, COVID-19 pandemic reached Europe. Many countries, including Poland imposed lockdown as a method of securing social distance between potentially infected. Stay-at-home orders and movement control within public space not only affected the touristm industry, but also the everyday life of the inhabitants. The hourly time-lapse from four HD webcams in Cracow (Poland) are used in this study to estimate how pedestrian activity changed during COVID-19 lockdown. The collected data covers the period from 9 June 2016 to 19 April 2020 and comes from various urban zones. One zone is tourist, one is residential and two are mixed. In the first stage of the analysis, a state-of-the-art machine learning algorithm (YOLOv3) is used to detect people. Additionally, a non-standard application of the YOLO method is proposed, oriented to the images from HD webcams. This approach (YOLOtiled) is less prone to pedestrian detection errors with the only drawback being the longer computation time. Splitting the HD image into smaller tiles increases the number of detected pedestrians by over 50%. In the second stage, the analysis of pedestrian activity before and during the COVID-19 lockdown is conducted for hourly, daily and weekly averages. Depending on the type of urban zone, the number of pedestrians decreased from 33% in residential zones to 85% in tourist zones located in the Old Town. The presented method allows for more efficient detection and counting of pedestrians from HD time-lapse webcam images compared to SSD, YOLOv3 and Faster R-CNN. The result of the research is a published database with the detected number of pedestrians from the four-year observation period for four locations in Cracow.
One Bit to Rule Them All : Binarizing the Reconstruction in 1-bit Compressive Sensing
Authors: Thomas Feuillen, Mike Davies, Luc Vandendorpe, Laurent Jacques
Date: 2020-08-17 00:00:00
Tasks: All, Binarization, Compressive Sensing

This work focuses on the reconstruction of sparse signals from their 1-bit measurements. The context is the one of 1-bit compressive sensing where the measurements amount to quantizing (dithered) random projections. Our main contribution shows that, in addition to the measurement process, we can additionally reconstruct the signal with a binarization of the sensing matrix. This binary representation of both the measurements and sensing matrix can dramatically simplify the hardware architecture on embedded systems, enabling cheaper and more power efficient alternatives. Within this framework, given a sensing matrix respecting the restricted isometry property (RIP), we prove that for any sparse signal the quantized projected back-projection (QPBP) algorithm achieves a reconstruction error decaying like O(m-1/2)when the number of measurements m increases. Simulations highlight the practicality of the developed scheme for different sensing scenarios, including random partial Fourier sensing.
An Architectural Design for Measurement Uncertainty Evaluation in Cyber-Physical Systems
Authors: Wenzel Pilar von Pilchau, Varun Gowtham, Maximilian Gruber, Matthias Riedl, Nikolaos-Stefanos Koutrakis, Jawad Tayyub, Jörg Hähner, Sascha Eichstädt, Eckart Uhlmann, Julian Polte, Volker Frey, Alexander Willner
Date: 2020-08-17 00:00:00

Several use cases from the areas of manufacturing and process industry, require highly accurate sensor data. As sensors always have some degree of uncertainty, methods are needed to increase their reliability. The common approach is to regularly calibrate the devices to enable traceability according to national standards and Syst\`eme international (SI) units - which follows costly processes. However, sensor networks can also be represented as Cyber Physical Systems (CPS) and a single sensor can have a digital representation (Digital Twin) to use its data further on. To propagate uncertainty in a reliable way in the network, we present a system architecture to communicate measurement uncertainties in sensor networks utilizing the concept of Asset Administration Shells alongside methods from the domain of Organic Computing. The presented approach contains methods for uncertainty propagation as well as concepts from the Machine Learning domain that combine the need for an accurate uncertainty estimation. The mathematical description of the metrological uncertainty of fused or propagated values can be seen as a first step towards the development of a harmonized approach for uncertainty in distributed CPSs in the context of Industrie 4.0. In this paper, we present basic use cases, conceptual ideas and an agenda of how to proceed further on.
Coverage Analysis for Dense Heterogeneous Networks with Cooperative NOMA
Authors: Chun-Hung Liu, Di-Chun Liang, Po-Chia Chen, Jie-Ru Yang
Date: 2020-08-14 00:00:00

In a heterogeneous cellular network (HetNet) consisting of $M$ tiers of densely-deployed base stations (BSs), consider that each of the BSs in the HetNet that are associated with multiple users is able to simultaneously schedule and serve two users in a downlink time slot by performing the (power-domain) non-orthogonal multiple access (NOMA) scheme. This paper aims at the preliminary study on the downlink coverage performance of the HetNet with the \textit{non-cooperative} and the proposed \textit{cooperative} NOMA schemes. First, we study the coverage probability of the NOMA users for the non-cooperative NOMA scheme in which no BSs are coordinated to jointly transmit the NOMA signals for a particular cell and the coverage probabilities of the two NOMA users of the BSs in each tier are derived. We show that the coverage probabilities can be largely reduced if allocated transmit powers for the NOMA users are not satisfied with some constraints. Next, we study and derive the coverage probabilities for the proposed cooperative NOMA scheme in which the void BSs that are not tagged by any users are coordinated to enhance the far NOMA user in a particular cell. Our analyses show that cooperative NOMA can significantly improve the coverage of all NOMA users as long as the transmit powers for the NOMA users are properly allocated.
Extended Mathematical Derivations: Decentralized Model-free Loss Minimization in Distribution Grids with the Use of Inverters
Authors: Ilgiz Murzakhanov, Spyros Chatzivasileiadis
Date: 2020-08-14 00:00:00

This document contains extended mathematical derivations for the communication-free and model-free algorithms that can actively control converter-connected devices, and can operate either as stand-alone or in combination with centralized optimization algorithms. We address the problem of loss minimization in distribution grids, and we analytically prove that our proposed algorithms reduce the total grid losses without any prior information about the network, requiring no communication, and based only on local measurements.
Dynamic nuclear structure emerges from chromatin crosslinks and motors
Authors: Kuang Liu, Alison E. Patteson, Edward J. Banigan, J. M. Schwarz
Date: 2020-08-17 00:00:00

The cell nucleus houses the chromosomes, which are linked to a soft shell of lamin filaments. Experiments indicate that correlated chromosome dynamics and nuclear shape fluctuations arise from motor activity. To identify the physical mechanisms, we develop a model of an active, crosslinked Rouse chain bound to a polymeric shell. System-sized correlated motions occur but require both motor activity {\it and} crosslinks. Contractile motors, in particular, enhance chromosome dynamics by driving anomalous density fluctuations. Nuclear shape fluctuations depend on motor strength, crosslinking, and chromosome-lamina binding. Therefore, complex chromatin dynamics and nuclear shape emerge from a minimal, active chromosome-lamina system.
Entropy-Based Doppler Centroid Estimation and Speckle Noise Reduction for Spaceborne SAR Imaging
Authors: Shahrokh Hamidi
Date: 2020-08-17 00:00:00
Tasks: Image Reconstruction

In this paper, we present strip-map mode spaceborne Synthetic Aperture Radar (SAR) imaging with the focus on Doppler centroid frequency estimation. The non-zero Doppler centroid frequency is the result of non-zero squint angle which if it is not compensated it can de-focus the image. We present an efficient method based on the entropy of the reconstructed image to estimate the fractional part of the Doppler centroid frequency. Furthermore, we discuss the speckle noise, which degrades the quality of the reconstructed images considerably, and attempt to alleviate its effect efficiently. Following the implementation of the speckle noise reduction algorithm, a significant improvement in the quality of the reconstructed images is achieved. Finally, we utilize the experimental data gathered from the RADARSAT-1 satellite of Vancouver Canada to verify the accuracy and effectiveness of the proposed techniques.
Host immune response driving SARS-CoV-2 evolution
Authors: Rui Wang, Yuta Hozumi, Yong-Hui Zheng, Changchuan Yin, Guo-Wei Wei
Date: 2020-08-17 00:00:00

The transmission and evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are of paramount importance to the controlling and combating of coronavirus disease 2019 (COVID-19) pandemic. Currently, near 15,000 SARS-CoV-2 single mutations have been recorded, having a great ramification to the development of diagnostics, vaccines, antibody therapies, and drugs. However, little is known about SARS-CoV-2 evolutionary characteristics and general trend. In this work, we present a comprehensive genotyping analysis of existing SARS-CoV-2 mutations. We reveal that host immune response via APOBEC and ADAR gene editing gives rise to near 65\% of recorded mutations. Additionally, we show that children under age five and the elderly may be at high risk from COVID-19 because of their overreacting to the viral infection. Moreover, we uncover that populations of Oceania and Africa react significantly more intensively to SARS-CoV-2 infection than those of Europe and Asia, which may explain why African Americans were shown to be at increased risk of dying from COVID-19, in addition to their high risk of getting sick from COVID-19 caused by systemic health and social inequities. Finally, our study indicates that for two viral genome sequences of the same origin, their evolution order may be determined from the ratio of mutation type C$>$T over T$>$C.
On the Complexity Reduction of Uplink Sparse Code Multiple Access for Spatial Modulation
Authors: Ibrahim Al-Nahhal, Octavia A. Dobre, Salama Ikki
Date: 2020-08-17 00:00:00
Tasks: Decoder

Multi-user spatial modulation (SM) assisted by sparse code multiple access (SCMA) has been recently proposed to provide uplink high spectral efficiency transmission. The message passing algorithm (MPA) is employed to detect the transmitted signals, which suffers from high complexity. This paper proposes three low-complexity algorithms for the first time to the SM-SCMA. The first algorithm is referred to as successive user detection (SUD), while the second algorithm is the modified version of SUD, namely modified SUD (MSUD). Then, for the first time, the tree-search of the SM-SCMA is constructed. Based on that tree-search, another variant of the sphere decoder (SD) is proposed for the SM-SCMA, referred to as fixed-complexity SD (FCSD). SUD provides a benchmark for decoding complexity at the expense of bit-error-rate (BER) performance. Further, MSUD slightly increases the complexity of SUD with a significant improvement in BER performance. Finally, FCSD provides a near-optimum BER with a considerable reduction of the complexity compared to the MPA decoder and also supports parallel hardware implementation. The proposed algorithms provide flexible design choices for practical implementation based on system design demands. The complexity analysis and Monte-Carlo simulations of the BER are provided for the proposed algorithms.
Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network
Authors: Eduardo Ramos-Pérez, Pablo J. Alonso-González, José Javier Núñez-Velázquez
Date: 2020-08-17 00:00:00
Tasks: Asset Management, Management

Currently, legal requirements demand that insurance companies increase their emphasis on monitoring the risks linked to the underwriting and asset management activities. Regarding underwriting risks, the main uncertainties that insurers must manage are related to the premium sufficiency to cover future claims and the adequacy of the current reserves to pay outstanding claims. Both risks are calibrated using stochastic models due to their nature. This paper introduces a reserving model based on a set of machine learning techniques such as Gradient Boosting, Random Forest and Artificial Neural Networks. These algorithms and other widely used reserving models are stacked to predict the shape of the runoff. To compute the deviation around a former prediction, a log-normal approach is combined with the suggested model. The empirical results demonstrate that the proposed methodology can be used to improve the performance of the traditional reserving techniques based on Bayesian statistics and a Chain Ladder, leading to a more accurate assessment of the reserving risk.
Feedback Gains modulate with Motor Memory Uncertainty
Authors: Sae Franklin, David W. Franklin
Date: 2020-08-17 00:00:00

A sudden change in dynamics produces large errors leading to increases in muscle co-contraction and feedback gains during early adaptation. We previously proposed that internal model uncertainty drives these changes, whereby the sensorimotor system reacts to the change in dynamics by up regulating stiffness and feedback gains to reduce the effect of model errors. However, these feedback gain increases have also been suggested to represent part of the adaptation mechanism. Here, we investigate this by examining changes in visuomotor feedback gains during gradual or abrupt force field adaptation. Participants grasped a robotic manipulandum and reached while a curl force field was introduced gradually or abruptly. Abrupt introduction of dynamics elicited large initial increases in kinematic error, muscle co-contraction and visuomotor feedback gains, while gradual introduction showed little initial change in these measures despite evidence of adaptation. After adaptation had plateaued,there was a change in the co-contraction and visuomotor feedback gains relative to null field movements, but no differences (apart from the final muscle activation pattern) between the abrupt and gradual introduction of dynamics. This suggests that the initial increase in feedback gains is not part of the adaptation process, but instead an automatic reactive response to internal model uncertainty. In contrast, the final level of feedback gains is a predictive tuning of the feedback gains to the external dynamics as part of the internal model adaptation. Together, the reactive and predictive feedback gains explain the wide variety of previous experimental results of feedback changes during adaptation.
Nonlinear Attitude Filter on SO(3): Fast Adaptation and Robustness
Authors: Ajay Singh, Trenton S. Sieb, James H. Howe, Hashim A. Hashim
Date: 2020-08-17 00:00:00

Nonlinear attitude filters have been recognized to have simpler structure and better tracking performance when compared with Gaussian attitude filters and other methods of attitude determination. A key element of nonlinear attitude filter design is the selection of error criteria. The conventional design of nonlinear attitude filters has a trade-off between fast adaptation and robustness. In this work, a new functional approach based on fuzzy rules for on-line continuous tuning of the nonlinear attitude filter adaptation gain is proposed. The input and output membership functions are optimally tuned using artificial bee colony optimization algorithm taking into account both attitude error and rate of change of attitude error. The proposed approach results of high adaptation gain at large error and small adaptation gain at small error. Thereby, the proposed approach allows fast convergence properties with high measures of robustness. The simulation results demonstrate that the proposed approach offers robust and high convergence capabilities against large error in initialization and uncertain measurements.
SWAN: Swarm-Based Low-Complexity Scheme for PAPR Reduction
Authors: Luis F. Abanto-Leon, Gek Hong Sim, Matthias Hollick, Amnart Boonkajay, Fumiyuki Adachi
Date: 2020-08-17 00:00:00

Cyclically shifted partial transmit sequences (CS-PTS) has conventionally been used in SISO systems for PAPR reduction of OFDM signals. Compared to other techniques, CS-PTS attains superior performance. Nevertheless, due to the exhaustive search requirement, it demands excessive computational complexity. In this paper, we adapt CS-PTS to operate in a MIMO framework, where singular value decomposition (SVD) precoding is employed. We also propose SWAN, a novel optimization method based on swarm intelligence to circumvent the exhaustive search. SWAN not only provides a significant reduction in computational complexity, but it also attains a fair balance between optimality and complexity. Through simulations, we show that SWAN achieves near-optimal performance at a much lower complexity than other competing approaches.