pwc-archive/papers-with-abstracts huggingface dataset:
Reconfigurable Intelligent Computational Surfaces: When Wave Propagation Control Meets Computing
Authors: Bo Yang, Xuelin Cao, Jindan Xu, Chongwen Huang, George C. Alexandropoulos, Linglong Dai, M'erouane Debbah, H. Vincent Poor, Chau Yuen
Date: 2022-08-09 00:00:00

The envisioned sixth-generation (6G) of wireless networks will involve an intelligent integration of communications and computing, thereby meeting the urgent demands of diverse applications. To realize the concept of the smart radio environment, reconfigurable intelligent surfaces (RISs) are a promising technology for offering programmable propagation of impinging electromagnetic signals via external control. However, the purely reflective nature of conventional RISs induces significant challenges in supporting computation-based applications, e.g., wave-based calculation and signal processing. To fulfil future communication and computing requirements, new materials are needed to complement the existing technologies of metasurfaces, enabling further diversification of electronics and their applications. In this event, we introduce the concept of reconfigurable intelligent computational surface (RICS), which is composed of two reconfigurable multifunctional layers: the `reconfigurable beamforming layer' which is responsible for tunable signal reflection, absorption, and refraction, and the `intelligence computation layer' that concentrates on metamaterials-based computing. By exploring the recent trends on computational metamaterials, RICSs have the potential to make joint communication and computation a reality. We further demonstrate two typical applications of RICSs for performing wireless spectrum sensing and secrecy signal processing. Future research challenges arising from the design and operation of RICSs are finally highlighted.
Recycling an anechoic pre-trained speech separation deep neural network for binaural dereverberation of a single source
Authors: Sania Gul, Muhammad Salman Khan, Syed Waqar Shah, Ata Ur-Rehman
Date: 2022-08-09 00:00:00
Tasks: Speech Separation

Reverberation results in reduced intelligibility for both normal and hearing-impaired listeners. This paper presents a novel psychoacoustic approach of dereverberation of a single speech source by recycling a pre-trained binaural anechoic speech separation neural network. As training the deep neural network (DNN) is a lengthy and computationally expensive process, the advantage of using a pre-trained separation network for dereverberation is that the network does not need to be retrained, saving both time and computational resources. The interaural cues of a reverberant source are given to this pretrained neural network to discriminate between the direct path signal and the reverberant speech. The results show an average improvement of 1.3% in signal intelligibility, 0.83 dB in SRMR (signal to reverberation energy ratio) and 0.16 points in perceptual evaluation of speech quality (PESQ) over other state-of-the-art signal processing dereverberation algorithms and 14% in intelligibility and 0.35 points in quality over orthogonal matching pursuit with spectral subtraction (OSS), a machine learning based dereverberation algorithm.
An Anchor-Free Detector for Continuous Speech Keyword Spotting
Authors: Zhiyuan Zhao, Chuanxin Tang, Chengdong Yao, Chong Luo
Date: 2022-08-09 00:00:00
Tasks: Keyword Spotting, object-detection, Object Detection

Continuous Speech Keyword Spotting (CSKWS) is a task to detect predefined keywords in a continuous speech. In this paper, we regard CSKWS as a one-dimensional object detection task and propose a novel anchor-free detector, named AF-KWS, to solve the problem. AF-KWS directly regresses the center locations and lengths of the keywords through a single-stage deep neural network. In particular, AF-KWS is tailored for this speech task as we introduce an auxiliary unknown class to exclude other words from non-speech or silent background. We have built two benchmark datasets named LibriTop-20 and continuous meeting analysis keywords (CMAK) dataset for CSKWS. Evaluations on these two datasets show that our proposed AF-KWS outperforms reference schemes by a large margin, and therefore provides a decent baseline for future research.
Loop Shaping with Scaled Relative Graphs
Authors: Thomas Chaffey, Fulvio Forni, Rodolphe Sepulchre
Date: 2022-08-09 00:00:00

The Scaled Relative Graph (SRG) is a generalization of the Nyquist diagram that may be plotted for nonlinear operators, and allows nonlinear robustness margins to be defined graphically. This abstract explores techniques for shaping the SRG of an operator in order to maximize these robustness margins.
On the incremental form of dissipativity
Authors: Rodolphe Sepulchre, Thomas Chaffey, Fulvio Forni
Date: 2022-08-09 00:00:00
Tasks: Form

Following the seminal work of Zames, the input-output theory of the 70s acknowledged that incremental properties (e.g. incremental gain) are the relevant quantities to study in nonlinear feedback system analysis. Yet, non-incremental analysis has dominated the use of dissipativity theory in nonlinear control from the 80s. Results connecting dissipativity theory and incremental analysis are scattered and progress has been limited. This abstract investigates whether this theoretical gap is of fundamental nature and considers new avenues to circumvent it.
Assigning Shadow Prices to Synthetic Inertia and Frequency Response Reserves from Renewable Energy Sources
Authors: Luis Badesa, Carlos Matamala, Yujing Zhou, Goran Strbac
Date: 2022-08-09 00:00:00

Modern electricity grids throughout the world, particularly in islands such as Great Britain, face a major problem on the road to decarbonisation: the significantly reduced level of system inertia due to integration of Renewable Energy Sources (RES). Given that most RES such as wind and solar are decoupled from the grid through power electronics converters, they do not naturally contribute to system inertia. However, RES could support grid stability through appropriately controlling the converters, but currently no market incentives exist for RES to provide this support. In this paper we develop a methodology to optimally clear a market of ancillary services for frequency control, while explicitly considering the participation of grid-forming and grid-following inverter-based technologies. We propose a mathematical framework that allows to compute shadow prices for ancillary services offered by a pool of diverse providers: synchronous and synthetic inertia, enhanced frequency response (e.g. from curtailed RES) and traditional primary frequency response (e.g. by thermal generators). Several case studies are run on a simplified Great Britain system, to illustrate the applicability and benefits of this pricing scheme.
Stability of linear multiagent systems with guaranteed steady-state performance
Authors: Gurmu Meseret Debele
Date: 2022-08-09 00:00:00

Gradual advancement of control technology gives rise to the studies of the stability of linear systems. The stability of the linear multiagent system is motivated by increasing utilization of agent dynamics together with the number of control protocols associated with each agent. To that respect, in this report, the idea of event-triggered control and the stability of the linear multiagent system under steady-state performance conditions will be presented. By applying a steady-state condition, the state of an agent in the closed-loop linear system will be discussed using fixed network topology both in discrete-time and continuous-time. Moreover, the system is also analyzed by employing the Lyapunov function methods, and then an average consensus of the system will also be realized. Finally, we will verify the system's average consensus and stability via a simulation example.
Fisher Matrix Based Fault Detection for PMUs Data in Power Grids
Authors: Ke Chen, Dandan Jiang, Bo wang, Hongxia Wang
Date: 2022-08-09 00:00:00
Tasks: Event Detection, Fault Detection

Abnormal event detection is critical in the safe operation of power system. In this paper, using the data collected from phasor measurement units (PMUs), two methods based on Fisher random matrix are proposed to detect faults in power grids. Firstly, the fault detection matrix is constructed and the event detection problem is reformatted as a two-sample covariance matrices test problem. Secondly, the central limit theorem for the linear spectral statistic of the Fisher matrix is derived and a test statistic for testing faults is proposed. To save computing resources, the screening step of fault interval based on the test statistic is designed to check the existence of faults. Then two point-by-point methods are proposed to determine the time of the fault in the interval. One method detects faults by checking whether the largest sample eigenvalue falls outside the supporting set of limiting spectral distribution of the standard Fisher matrix, which can detect the faults with higher accuracy. The other method tests the faults based on the statistic proposed, which has a faster detection speed. Compared with existing works, the simulation results illustrate that two methods proposed in this paper cost less computational time and provide a higher degree of accuracy.
Robust Output Feedback Control Design in the Presence of Sporadic Measurements
Authors: Roberto Merco, Francesco Ferrante, Ricardo G. Sanfelice, Pierluigi Pisu
Date: 2022-08-09 00:00:00

Output feedback control design for linear time-invariant systems in the presence of sporadic measurements and exogenous perturbations is addressed. To cope with the sporadic availability of measurements of the output, a hybrid dynamic output feedback controller equipped with a holding device whose state is reset when a new measurement is available is designed. The closed-loop system, resulting from the interconnection of the controller and the plant, is augmented with a timer variable triggering the arrival of new measurements and its properties are analyzed using hybrid system tools. Building upon Lyapunov theory for hybrid systems, sufficient conditions for internal and L2 input-to-output stability are proposed. An LMI-based design methodology for the co-design of the gains of the controller and the parameters of the holding device is presented. The effectiveness of the proposed LMI-based design approach is showcased in a numerical example.
Machine Learning for LiDAR-Based Navigation System
Authors: Farhad Aghili
Date: 2022-08-09 00:00:00
Tasks: Fault Detection, Pose Tracking

This paper presents a robust 6-DOF relative navigation by combining the iterative closet point (ICP) registration algorithm and a noise-adaptive Kalman filter (AKF) in a closed-loop configuration together with measurements from a laser scanner and an inertial measurement unit (IMU). In this approach, the fine-alignment phase of the registration is integrated with the filter innovation step for estimation correction while the filter estimate propagation provides the coarse alignment needed to find the corresponding points at the beginning of ICP iteration cycle. The convergence of the ICP point matching is monitored by a fault-detection logic and the covariance associated with the ICP alignment error is estimated by a recursive algorithm. This ICP enhancement has proven to improve robustness and accuracy of the pose tracking performance and to automatically recover correct alignment whenever the tracking is lost. The Kalman filter estimator is designed so as to identify the required parameters such as IMU biases and location of the spacecraft center-of-mass (CoM).
Perimeter control in a mixed bimodal bathtub model
Authors: Takao Dantsuji, Yuki Takayama, Daisuke Fukuda
Date: 2022-08-09 00:00:00
Tasks: model

Perimeter control involves monitoring network-wide traffic and regulating traffic inflow to alleviate hypercongestion. Implementation of transit priority with perimeter control measures, which allow transit into a controlled area without queuing at the perimeter boundary, is an effective strategy in bimodal transportation systems. However, travelers' behavior changes in response to perimeter control strategies, such as shifts in their departure times and transportation modes, have not been fully investigated. Therefore, important questions remain, such as the use of transit during perimeter control with transit priority. This paper examines the travelers' behavior changes in response to perimeter control with transit priority in a mixed bimodal transportation system with cars and flexible route transit (FRT) vehicles. We model departure time and transportation mode choices in such a transportation system with hypercongestion and discomfort in FRT (called the mixed bimodal bathtub model). Initially, we investigate the properties of dynamic user equilibrium without perimeter control. Then, we study the equilibrium patterns during perimeter control with transit priority. Unlike existing works, we find that the number of FRT passengers decreases with time toward the desired arrival time and that FRT may not be used around the peak of rush hour. Furthermore, transit priority may not be sufficient to promote the use of FRT, and additional incentive such as subsidy for lower fares may be required to encourage FRT use during perimeter control. Finally, we show that operating many FRT vehicles does not always decrease the equilibrium cost, even under perimeter control with transit priority.
Parameter Estimation in Ill-conditioned Low-inertia Power Systems
Authors: Rajasekhar Anguluri, Lalitha Sankar, Oliver Kosut
Date: 2022-08-09 00:00:00
Tasks: Connectivity Estimation, parameter estimation

This paper examines model parameter estimation in dynamic power systems whose governing electro-mechanical equations are ill-conditioned or singular. This ill-conditioning is because of converter-interfaced power systems generators' zero or small inertia contribution. Consequently, the overall system inertia decreases, resulting in low-inertia power systems. We show that the standard state-space model based on least squares or subspace estimators fails to exist for these models. We overcome this challenge by considering a least-squares estimator directly on the coupled swing-equation model but not on its transformed first-order state-space form. We specifically focus on estimating inertia (mechanical and virtual) and damping constants, although our method is general enough for estimating other parameters. Our theoretical analysis highlights the role of network topology on the parameter estimates of an individual generator. For generators with greater connectivity, estimation of the associated parameters is more susceptible to variations in other generator states. Furthermore, we numerically show that estimating the parameters by ignoring their ill-conditioning aspects yields highly unreliable results.
Bullwhip Effect of Supply Networks: Joint Impact of Network Structure and Market Demand
Authors: Jin-Zhu Yü, Chencheng Cai, Jianxi Gao
Date: 2022-08-08 00:00:00
Tasks: Position

The progressive amplification of fluctuations in demand as the demand travels upstream the supply chains is known as the bullwhip effect. We first analytically characterize the bullwhip effect in general supply chain networks in two cases: (i) all suppliers have a unique layer position, where our method is founded on the control-theoretic approach, and (ii) not all suppliers have a unique layer position due to the presence of intra-layer links or inter-layer links between suppliers that are not positioned in consecutive layers, where we use both the absorbing Markov chain and the control-theoretic approach. We then investigate how network structures impact the BWE of supply chain networks. In particular, we analytically show that (i) if the market demand is generated from the same stationary process, the structure of supply networks does not affect the layer-wise bullwhip effect of supply networks, and (ii) if the market demand is generated from different stationary or non-stationary market processes, wider supply networks lead to a lower level of layer-wise bullwhip effect. Finally, numerical simulations are used to validate our propositions.
Exact lattice-based stochastic cell culture simulation algorithms incorporating spontaneous and contact-dependent reactions
Authors: Peter Boldog
Date: 2022-08-09 00:00:00
Tasks: Benchmarking, Cultural Vocal Bursts Intensity Prediction

In this paper, we address the modeling issues of cell movement and division with a special focus on the phenomenon of volume exclusion in a lattice-based, exact stochastic simulation framework. We propose a new exact method, called Reduced Rate Method -- RRM, that is substantially quicker than the previously used exclusion method, for large number of cells. In addition, we introduce three novel reaction types: the contact-inhibited, the contact-promoted, and the spontaneous reactions. To the best of our knowledge, these reaction types have not been taken into account in lattice-based stochastic simulations of cell cultures. These new types of events may be easily applied to complicated systems, enabling the generation of biologically feasible stochastic cell culture simulations. Furthermore, we show that the exclusion algorithm and our RRM algorithm are mathematically equivalent in the sense that the next reaction to be realized and the corresponding sojourn time both belong to the same reaction and time distributions in the two approaches -- even with the newly introduced reaction types. Exact, agent-based, stochastic methods of cell culture simulations seem to be undervalued and are mostly used as benchmarking tools to validate deterministic approximations of the corresponding stochastic models. Our proposed methods are exact, they are easy to implement, have a high predictive value, and can be conveniently extended with new features. Therefore, these approaches promise a great potential.
Energy landscape analysis of cardiac fibrillation wave dynamics using pairwise maximum entropy model
Authors: Euijun Song
Date: 2018-09-26 00:00:00
Tasks: Rhythm

Cardiac fibrillation is characterized by chaotic and disintegrated spiral wave dynamics patterns, whereas sinus rhythm shows synchronized excitation patterns. To determine functional correlations among cardiomyocytes during complex fibrillation states, we applied a pairwise maximum entropy model (MEM) to the 2D numerical simulation data of human atrial fibrillation. We then constructed an energy landscape and estimated a hierarchical structure among the different local minima (attractors) to explain the dynamic properties of cardiac fibrillation. The MEM could describe the wave dynamics of sinus rhythm, single stable rotor, and single rotor with wavebreak (both accuracy and reliability>0.9), but not the multiple random wavelet case. The energy landscapes exhibited unique profiles of local minima and energy barriers, characterizing the spatiotemporal patterns of cardiac fibrillation dynamics. The pairwise MEM-based energy landscape analysis provides reliable explanations of complex nonlinear dynamics of cardiac fibrillation, which might be determined by the presence of a 'driver' such as a sinus node or rotor.
Neural signal propagation atlas of C. elegans
Authors: Francesco Randi, Anuj K. Sharma, Sophie Dvali, Andrew M. Leifer
Date: 2022-08-09 00:00:00
Tasks: Anatomy

A fundamental problem in neuroscience is understanding how a network's properties dictate its function. Connectomics provides one avenue to predict nervous system function. To test this explicitly, we systematically measure signal propagation in 23,427 pairs of neurons across the head of the nematode Caenorhabditis elegans by direct optogenetic activation and simultaneous whole-brain calcium imaging. We measure the sign (excitatory or inhibitory), strength, temporal properties, and causal direction of signal propagation between these neurons to create a functional atlas. We find that signal propagation differs from predictions based on anatomy. Using mutants, we show that extrasynaptic signaling not visible from anatomy contributes to this difference. We identify many instances of dense-core-vesicle dependent signaling on seconds-or-less timescales that evoke acute calcium transients often where no direct wired connection exists but where relevant neuropeptides and receptors are expressed. We propose that here extrasynaptically released neuropeptides serve a similar function as that of classical neurotransmitters. Finally, our measured signal propagation atlas better predicts neural dynamics of spontaneous activity than does anatomy. We conclude that both synaptic and extrasynaptic signaling drive neural dynamics on short timescales and that measurement of evoked signal propagation are critical for interpreting neural function.
Quantum Finance: a tutorial on quantum computing applied to the financial market
Authors: Askery Canabarro, Taysa M. Mendonça, Ranieri Nery, George Moreno, Anton S. Albino, Gleydson F. de Jesus, Rafael Chaves
Date: 2022-08-08 00:00:00
Tasks: Portfolio Optimization

Previously only considered a frontier area of Physics, nowadays quantum computing is one of the fastest growing research field, precisely because of its technological applications in optimization problems, machine learning, information security and simulations. The goal of this article is to introduce the fundamentals of quantum computing, focusing on a promising quantum algorithm and its application to a financial market problem. More specifically, we discuss the portfolio optimization problem using the \textit{Quantum Approximate Optimization Algorithm} (QAOA). We not only describe the main concepts involved but also consider simple practical examples, involving financial assets available on the Brazilian stock exchange, with codes, both classic and quantum, freely available as a Jupyter Notebook. We also analyze in details the quality of the combinatorial portfolio optimization solutions through QAOA using SENAI/CIMATEC's ATOS QLM quantum simulator.
The Nexus between Job Burnout and Emotional Intelligence on Turnover Intention in Oil and Gas Companies in the UAE
Authors: Anas Abudaqa, Mohd Faiz Hilmi, Norziani Dahalan
Date: 2022-08-06 00:00:00
Tasks: Emotional Intelligence

Currently, job satisfaction and turnover intentions are the significant issues for oil and gas companies in the United Arab Emirates (UAE). These issues need to be addressed soon for the performance of the oil and gas companies. Thus, the aim related to the current study is to examine the impact of job burnout, emotional intelligence, and job satisfaction on the turnover intentions of the oil and gas companies in the UAE. The goals of this research also include the examination of mediating the influence of job satisfaction alongside the nexus of job burnout and turnover intentions of the oil and gas companies in the UAE. The questionnaire method was adopted to collect the data from the respondents, and Smart-PLS were employed to analyse the data. The results show that job burnout, emotional intelligence, and job satisfaction have a positive association with turnover intentions. In contrast, job satisfaction positively mediates the nexus between job burnout and turnover intentions. These results provide the guidelines to the policymakers that they should enhance their focus on job satisfaction and turnover intentions of the employees that improve the firm performance.
Costly Evidence and Discretionary Disclosure
Authors: Mark Whitmeyer, Kun Zhang
Date: 2022-08-09 00:00:00

A sender flexibly acquires evidence--which she may pay a third party to certify--to disclose to a receiver. When evidence acquisition is overt, the receiver observes the evidence gathering process irrespective of whether its outcome is certified. When acquisition is covert, the receiver does not. In contrast to the case with exogenous evidence, the receiver prefers a strictly positive certification cost. As acquisition costs vanish, equilibria converge to the Pareto-worst free-learning equilibrium. The receiver always prefers covert to overt evidence acquisition.