pwc-archive/papers-with-abstracts huggingface dataset:
A Novel RIS-Aided EMF-Aware Beamforming Using Directional Spreading, Truncation and Boosting
Authors: Nour Awarkeh, Dinh-Thuy Phan-Huy, Raphael Visoz, Marco Di Renzo
Date: 2022-06-14 00:00:00

This paper addresses a drawback of massive multiple-input multiple-output Maximum Ratio Transmission beamforming. In some propagation conditions, when the base station serves the same target user equipment for a long period, it reduces the transmit power (and degrades the received power) to avoid creating high exposure regions located in the vicinity of the antenna and concentrated in few directions (corresponding to the best propagation paths between the antenna and the receiver). In this paper, we propose a novel electromagnetic field aware beamforming scheme, which (i) spreads the beamforming radiation pattern in the angular domain by adding artificial propagation paths thanks to reconfigurable intelligent surfaces, (ii) truncates the pattern in strong directions, and (iii) boosts it in weak directions. Compared to existing solutions, it maximizes the received power. However, it also consumes more power. Finally, truncation alone is the best trade-off between received power and energy efficiency, under exposure constrain.
Coevolutionary Dynamics of Actions and Opinions in Social Networks
Authors: Hassan Dehghani Aghbolagh, Mengbin Ye, Lorenzo Zino, Ming Cao, Zhiyong Chen
Date: 2022-06-15 00:00:00
Tasks: Decision Making

Empirical studies suggest a deep intertwining between opinion formation and decision-making processes, but these have been treated as separate problems in the study of dynamical models for social networks. In this paper, we bridge the gap in the literature by proposing a novel coevolutionary model, in which each individual selects an action from a binary set and has an opinion on which action they prefer. Actions and opinions coevolve on a two-layer network. For homogeneous parameters, undirected networks, and under reasonable assumptions on the asynchronous updating mechanics, we prove that the coevolutionary dynamics is an ordinal potential game, enabling analysis via potential game theory. Specifically, we establish global convergence to the Nash equilibria of the game, proving that actions converge in a finite number of time steps, while opinions converge asymptotically. Next, we provide sufficient conditions for the existence of, and convergence to, polarized equilibria, whereby the population splits into two communities, each selecting and supporting one of the actions. Finally, we use simulations to examine the social psychological phenomenon of pluralistic ignorance.
Self-Assessment for Single-Object Tracking in Clutter Using Subjective Logic
Authors: Thomas Griebel, Johannes Müller, Paul Geisler, Charlotte Hermann, Martin Herrmann, Michael Buchholz, Klaus Dietmayer
Date: 2022-06-15 00:00:00
Tasks: Decision Making, Object Tracking

Reliable tracking algorithms are essential for automated driving. However, the existing consistency measures are not sufficient to meet the increasing safety demands in the automotive sector. Therefore, this work presents a novel method for self-assessment of single-object tracking in clutter based on Kalman filtering and subjective logic. A key feature of the approach is that it additionally provides a measure of the collected statistical evidence in its online reliability scores. In this way, various aspects of reliability, such as the correctness of the assumed measurement noise, detection probability, and clutter rate, can be monitored in addition to the overall assessment based on the available evidence. Here, we present a mathematical derivation of the reference distribution used in our self-assessment module for our studied problem. Moreover, we introduce a formula that describes how a threshold should be chosen for the degree of conflict, the subjective logic comparison measure used for the reliability decision making. Our approach is evaluated in a challenging simulation scenario designed to model adverse weather conditions. The simulations show that our method can significantly improve the reliability checking of single-object tracking in clutter in several aspects.
End-to-End Voice Conversion with Information Perturbation
Authors: Qicong Xie, Shan Yang, Yi Lei, Lei Xie, Dan Su
Date: 2022-06-15 00:00:00
Tasks: Voice Conversion

The ideal goal of voice conversion is to convert the source speaker's speech to sound naturally like the target speaker while maintaining the linguistic content and the prosody of the source speech. However, current approaches are insufficient to achieve comprehensive source prosody transfer and target speaker timbre preservation in the converted speech, and the quality of the converted speech is also unsatisfied due to the mismatch between the acoustic model and the vocoder. In this paper, we leverage the recent advances in information perturbation and propose a fully end-to-end approach to conduct high-quality voice conversion. We first adopt information perturbation to remove speaker-related information in the source speech to disentangle speaker timbre and linguistic content and thus the linguistic information is subsequently modeled by a content encoder. To better transfer the prosody of the source speech to the target, we particularly introduce a speaker-related pitch encoder which can maintain the general pitch pattern of the source speaker while flexibly modifying the pitch intensity of the generated speech. Finally, one-shot voice conversion is set up through continuous speaker space modeling. Experimental results indicate that the proposed end-to-end approach significantly outperforms the state-of-the-art models in terms of intelligibility, naturalness, and speaker similarity.
Residual Language Model for End-to-end Speech Recognition
Authors: Emiru Tsunoo, Yosuke Kashiwagi, Chaitanya Narisetty, Shinji Watanabe
Date: 2022-06-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Domain Adaptation, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

End-to-end automatic speech recognition suffers from adaptation to unknown target domain speech despite being trained with a large amount of paired audio--text data. Recent studies estimate a linguistic bias of the model as the internal language model (LM). To effectively adapt to the target domain, the internal LM is subtracted from the posterior during inference and fused with an external target-domain LM. However, this fusion complicates the inference and the estimation of the internal LM may not always be accurate. In this paper, we propose a simple external LM fusion method for domain adaptation, which considers the internal LM estimation in its training. We directly model the residual factor of the external and internal LMs, namely the residual LM. To stably train the residual LM, we propose smoothing the estimated internal LM and optimizing it with a combination of cross-entropy and mean-squared-error losses, which consider the statistical behaviors of the internal LM in the target domain data. We experimentally confirmed that the proposed residual LM performs better than the internal LM estimation in most of the cross-domain and intra-domain scenarios.
Dynamic State Estimation of Nonlinear Differential Algebraic Equation Models of Power Networks
Authors: Muhammad Nadeem, Sebastian A. Nugroho, Ahmad F. Taha
Date: 2022-06-15 00:00:00
Tasks: State Estimation

This paper investigates the joint problems of dynamic state estimation of algebraic variables (voltage and phase angle) and generator states (rotor angle and frequency) of nonlinear differential algebraic equation (NDAE) power network models, under uncertainty. Traditionally, these two problems have been decoupled due to complexity of handling NDAE models. In particular, this paper offers the first attempt to solve the aforementioned problem in a coupled approach where the algebraic and generator states estimates are simultaneously computed. The proposed estimation algorithm herein is endowed with the following properties: (i) it is fairly simple to implement and based on well-understood Lyapunov theory; (ii) considers various sources of uncertainty from generator control inputs, loads, renewables, process and measurement noise; (iii) models phasor measurement unit installations at arbitrary buses; and (iv) is computationally less intensive than the decoupled approach in the literature.
Optimal Synthesis of LTI Koopman Models for Nonlinear Systems with Inputs
Authors: Lucian Cristian Iacob, Roland Tóth, Maarten Schoukens
Date: 2022-06-15 00:00:00

A popular technique used to obtain linear representations of nonlinear systems is the so-called Koopman approach, where the nonlinear dynamics are lifted to a (possibly infinite dimensional) linear space through nonlinear functions called observables. In the lifted space, the dynamics are linear and represented by a so-called Koopman operator. While the Koopman theory was originally introduced for autonomous systems, it has been widely used to derive linear time-invariant (LTI) models for nonlinear systems with inputs through various approximation schemes such as the extended dynamics mode decomposition (EDMD). However, recent extensions of the Koopman theory show that the lifting process for such systems results in a linear parameter-varying (LPV) model instead of an LTI form. As LTI Koopman model based control has been successfully used in practice and it is generally temping to use such LTI descriptions of nonlinear systems, due to the simplicity of the associated control tool chain, a systematic approach is needed to synthesise optimal LTI approximations of LPV Koopman models compared to the ad-hoc schemes such as EDMD, which is based on least-squares regression. In this work, we introduce optimal LTI Koopman approximations of exact Koopman models of nonlinear systems with inputs by using l2-gain and generalized H2 norm performance measures. We demonstrate the advantages of the proposed Koopman modelling procedure compared to EDMD.
Discrete-time Layered-network Epidemics Model with Time-varying Transition Rates and Multiple Resources
Authors: Shaoxuan Cui, Fangzhou Liu, Hildeberto Jardón-Kojakhmetov, Ming Cao
Date: 2022-06-15 00:00:00

This paper studies a discrete-time time-varying multi-layer networked SIWS (susceptible-infected-water-susceptible) model with multiple resources under both single-virus and competing multi-virus settings. Besides the human-to-human interaction, we also consider that the disease can diffuse on different types of medium. We use \emph{resources} to refer to any media that the pathogen of a virus can spread through, and do not restrict the resource only to be water. In the single-virus case, we give a full analysis of the system's behaviour related to its healthy state and endemic equilibrium. In the multi-virus case, we show analytically that different equilibria appear driven by the competition among all viruses. We also show that some analytical results of the time-invariant system can be expanded into time-varying cases. Finally, we illustrate the results through some simulations.
Modelling of AC/DC Interactions of Converter-Interfaced Resources for Harmonic Power-Flow Studies in Microgrids
Authors: Johanna Kristin Maria Becker, Andreas Martin Kettner, Yihui Zuo, Federico Cecati, Sante Pugliese, Marco Liserre, Mario Paolone
Date: 2022-06-15 00:00:00

Power distribution systems experience a large-scale integration of Converter-Interfaced Distributed Energy Resources (CIDERs). As acknowledged by recent literature, the interaction of individual CIDER components and different CIDERs through the grid can lead to undesirable amplification of harmonic frequencies and, ultimately, compromise the distribution system stability. In this context, the interaction of the DC and AC sides of CIDERs has been shown to have a significant impact. In order to analyze and support the mitigation of such phenomena, the authors of this paper recently proposed a Harmonic Power-Flow (HPF) framework for polyphase grids with a high share of CIDERs. The framework considers the coupling between harmonics, but ignores the DC-side response of the CIDERs. Modelling the DC side and AC/DC converter introduces a nonlinearity into the CIDER model that needs to be approximated for the numerical solution of the HPF. This paper extends the CIDER model and HPF framework to address this aspect, whose inclusion is non-trivial. The extended HPF method is applied to a modified version of the CIGRE low-voltage benchmark microgrid. The results are compared to (i) time-domain simulations with Simulink, (ii) the predecessor of the extended HPF which neglects the DC side, and (iii) a classical decoupled HPF.
Attacks on Perception-Based Control Systems: Modeling and Fundamental Limits
Authors: Amir Khazraei, Henry Pfister, Miroslav Pajic
Date: 2022-06-14 00:00:00

We study the performance of perception-based control systems in the presence of attacks, and provide methods for modeling and analysis of their resiliency to stealthy attacks on both physical and perception-based sensing. Specifically, we consider a general setup with a nonlinear affine physical plant controlled with a perception-based controller that maps both the physical (e.g., IMUs) and perceptual (e.g., camera) sensing to the control input; the system is also equipped with a statistical or learning-based anomaly detector (AD). We model the attacks in the most general form, and introduce the notions of attack effectiveness and stealthiness independent of the used AD. In such setting, we consider attacks with different levels of runtime knowledge about the plant. We find sufficient conditions for existence of stealthy effective attacks that force the plant into an unsafe region without being detected by any AD. We show that as the open-loop unstable plant dynamics diverges faster and the closed-loop system converges faster to an equilibrium point, the system is more vulnerable to effective stealthy attacks. Also, depending on runtime information available to the attacker, the probability of attack remaining stealthy can be arbitrarily close to one, if the attacker's estimate of the plant's state is arbitrarily close to the true state; when an accurate estimate of the plant state is not available, the stealthiness level depends on the control performance in attack-free operation.
Comparison of Different Configurations of Saturated Core Fault Current Limiters in a Power Grid by Numerical Method
Authors: Aydin Zaboli
Date: 2022-06-14 00:00:00

Short circuit fault currents are increasing due to growing demand for electricity and high complexity in power systems. Because the fault currents reach the highest value which the breakers are unable to restrict, the electrical grid security is under jeopardy. By entering a limiting impedance into a transmission line in series, these impedances restrict the rising amounts of fault currents to levels that are acceptable. Saturated core fault current limiters (SCFCLs) are a pivotal tool for limiting fault currents rise in power networks that have good performance characteristics. In a normal condition, these limiters have slight effects on the system and can effectively limit short circuit currents when occur. In this chapter, various structures of SCFCLs with different arrangements of ac windings & dc windings are presented and the currents passing through the FCLs under the normal and faulty system conditions are assessed and compared. The flux density in various regions of the core in different arrangements has been investigated as well and the desired analyzes have been performed. Simulation will be presented based on COMSOL Multiphysics 5.4, a finite element software package which can provide a precious assessment to compare these protective devices with different configurations.
Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey
Authors: Yue Xiang, Shenghong Gu, Dongtao Cao
Date: 2022-06-15 00:00:00
Tasks: Triplet

We apply the variational autoencoder (VAE) to the LAMOST-K2 low-resolution spectra to detect the magnetic activity of the stars in the K2 field. After the training on the spectra of the selected inactive stars, the VAE model can efficiently generate the synthetic reference templates needed by the spectral subtraction procedure, without knowing any stellar parameters. Then we detect the peculiar spectral features, such as chromospheric emissions, strong nebular emissions and lithium absorptions, in our sample. We measure the emissions of the chromospheric activity indicators, H$\alpha$ and Ca II infrared triplet (IRT) lines, to quantify the stellar magnetic activity. The excess emissions of H$\alpha$ and Ca II IRT lines of the active stars are correlated well to the rotational periods and the amplitudes of light curves derived from the K2 photometry. We degrade the LAMOST spectra to simulate the slitless spectra of the China Space Station Telescope (CSST) and apply the VAE to the simulated data. For cool active stars, we reveal a good agreement between the equivalent widths (EWs) of H$\alpha$ line derived from the spectra with two resolutions. The result indicates the ability of identifying the magnetically active stars in the future CSST survey, which will deliver an unprecedented large database of low-resolution spectra as well as simultaneous multi-band photometry of stars.
Comparison between an exact and a heuristic neural mass model with second order synapses
Authors: Pau Clusella, Elif Köksal-Ersöz, Jordi Garcia-Ojalvo, Giulio Ruffini
Date: 2022-06-15 00:00:00

Neural mass models (NMMs) are designed to reproduce the collective dynamics of neuronal populations. A common framework for NMMs assumes heuristically that the output firing rate of a neural population can be described by a static nonlinear transfer function (NMM1). However, a recent exact mean-field theory for quadratic integrate-and-fire (QIF) neurons challenges this view by showing that the mean firing rate is not a static function of the neuronal state but follows two coupled nonlinear differential equations (NMM2). Here we analyze and compare these two descriptions in the presence of second-order synaptic dynamics. First, we derive the mathematical equivalence between the two models in the infinitely slow synapse limit, i.e., we show that NMM1 is an approximation of NMM2 in this regime. Next, we evaluate the applicability of this limit in the context of realistic physiological parameter values by analyzing the dynamics of models with inhibitory or excitatory synapses. We show that NMM1 fails to reproduce important dynamical features of the exact model, such as the self-sustained oscillations of an inhibitory interneuron QIF network. Furthermore, in the exact model but not in the limit one, stimulation of a pyramidal cell population induces resonant oscillatory activity whose peak frequency and amplitude increase with the self-coupling gain and the external excitatory input. This may play a role in the enhanced response of densely connected networks to weak uniform inputs, such as the electric fields produced by non-invasive brain stimulation.
On the Relationship Between Ground- and Satellite- Based Global Horizontal Irradiance
Authors: Mayank Jain, Deepak Joel Yericherla, Soumyabrata Dev
Date: 2022-06-15 00:00:00
Tasks: Weather Forecasting

Global horizontal irradiance (GHI) plays a significant role in maintaining the earth's ecological balance and generating electricity in photovoltaic systems. While the satellites have more range, they have been shown to over/under-estimate the true values of GHI that are observed at the ground-based stations. Hence, this study aims at analyzing the relationship between these two sources of GHI data in order to better and effectively utilize the reach of satellites for GHI analysis. The paper identifies a near linear relationship between the two and thereby concludes that an approximate mapping from satellite- to ground-based GHI values can be obtained.
IoT- Based Low-Cost Soil Moisture and Soil Temperature Monitoring System
Authors: Guruprasad Deshpande, Mangesh Goswami, Jayesh Kolhe, Vishal Khandagale, Darshan Khope, Gargi Patel, Radhika Doijad, Rajani P. K., Milind Mujumdar, Bhupendra Bahadur Singh, Naresh Ganeshi
Date: 2022-05-23 00:00:00
Tasks: Data Visualization

Soil moisture (SM) is referred to as a finite amount of water molecules within the pore spaces and it is a crucial parameter of Hydro-Meteorological processes. The behaviour of soil moisture water changes spatially and temporally in response to topography, soil characteristics, and climate[1]. Soil moisture is overseen by various hydro-meteorological factors that vary vertically with depth, laterally across terrestrial shapes, and temporarily in feedback to the climate. The precise monitoring and quantification of high-resolution surface and subsurface soil moisture observations are very important [13]. This paper highlights the outcomes of the fieldwork carried out at IITM, Pune, wherein we have developed a soil moisture and temperature measurement system using Raspberry Pi and the Internet of things (IoT). The development is classified into three stages, the first stage includes the assembly of the sensor with the microprocessor. The deployment of the low-cost system, data generation, and communication through a wireless sensor network is part of the second stage. Finally, the third stage includes real-time data visualization using a mobile application and data server for analysing soil moisture and temperature. The soil moisture profile obtained through the sensor deployed is highly correlated (r=.9) with in-situ gravimetric observations, having a root mean square error (RMSE) of about 3.1%. Similarly, the temperature observations are well-matched with the in-situ standard temperature observation. Here we present the preliminary results and compare the accuracy with the state-of-the-art sensors.
Exploring Chemical Space with Score-based Out-of-distribution Generation
Authors: Seul Lee, Jaehyeong Jo, Sung Ju Hwang
Date: 2022-06-06 00:00:00
Tasks: Drug Discovery

A well-known limitation of existing molecular generative models is that the generated molecules highly resemble those in the training set. To generate truly novel molecules that may have even better properties for de novo drug discovery, more powerful exploration in the chemical space is necessary. To this end, we propose Molecular Out-Of-distribution Diffusion(MOOD), a score-based diffusion scheme that incorporates out-of-distribution (OOD) control in the generative stochastic differential equation (SDE) with simple control of a hyperparameter, thus requires no additional costs. Since some novel molecules may not meet the basic requirements of real-world drugs, MOOD performs conditional generation by utilizing the gradients from a property predictor that guides the reverse-time diffusion process to high-scoring regions according to target properties such as protein-ligand interactions, drug-likeness, and synthesizability. This allows MOOD to search for novel and meaningful molecules rather than generating unseen yet trivial ones. We experimentally validate that MOOD is able to explore the chemical space beyond the training distribution, generating molecules that outscore ones found with existing methods, and even the top 0.01% of the original training pool. Our code is available at https://github.com/SeulLee05/MOOD.