pwc-archive/papers-with-abstracts huggingface dataset:
Deep Mesh Prior: Unsupervised Mesh Restoration using Graph Convolutional Networks
Authors: Shota Hattori, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki
Date: 2021-07-02 00:00:00
Tasks: Denoising

This paper addresses mesh restoration problems, i.e., denoising and completion, by learning self-similarity in an unsupervised manner. For this purpose, the proposed method, which we refer to as Deep Mesh Prior, uses a graph convolutional network on meshes to learn the self-similarity. The network takes a single incomplete mesh as input data and directly outputs the reconstructed mesh without being trained using large-scale datasets. Our method does not use any intermediate representations such as an implicit field because the whole process works on a mesh. We demonstrate that our unsupervised method performs equally well or even better than the state-of-the-art methods using large-scale datasets.
Representing choice functions by a total hyper-order
Authors: Daniel Lehmann
Date: 2021-07-06 00:00:00

Choice functions over a set $X$ that satisfy the Outcast, a.k.a. Aizerman, property are exactly those that attach to any set its maximal subset relative to some total order of ${2}^{X}$.
Improving Speech Recognition Accuracy of Local POI Using Geographical Models
Authors: Songjun Cao, Yike Zhang, Xiaobing Feng, Long Ma
Date: 2021-07-07 00:00:00
Tasks: speech-recognition, Speech Recognition

Nowadays voice search for points of interest (POI) is becoming increasingly popular. However, speech recognition for local POI has remained to be a challenge due to multi-dialect and massive POI. This paper improves speech recognition accuracy for local POI from two aspects. Firstly, a geographic acoustic model (Geo-AM) is proposed. The Geo-AM deals with multi-dialect problem using dialect-specific input feature and dialect-specific top layer. Secondly, a group of geo-specific language models (Geo-LMs) are integrated into our speech recognition system to improve recognition accuracy of long tail and homophone POI. During decoding, specific language models are selected on demand according to users' geographic location. Experiments show that the proposed Geo-AM achieves 6.5%$\sim$10.1% relative character error rate (CER) reduction on an accent testset and the proposed Geo-AM and Geo-LM totally achieve over 18.7% relative CER reduction on Tencent Map task.
Post-Metering Value-Added Services for Low Voltage Electricity Users: Lessons Learned From the Italian Experience of CHAIN 2
Authors: Daniele Serra, Daniele Mardero, Luca Di Stefano, Samuele Grillo
Date: 2021-07-07 00:00:00

Electrical energy consumption data accessibility for low voltage end users is one of the pillars of smart grids. In some countries, despite the presence of smart meters, a fragmentary data availability and/or the lack of standardization hinders the creation of post-metering value-added services and confines such innovative solutions to the prototypal and experimental level. We take inspiration from the technology adopted in Italy, where the national regulatory authority actively supported the definition of a solution agreed upon by all the involved stakeholders. In this context, smart meters are enabled to convey data to low voltage end users through a power line communication channel (CHAIN 2) in near real-time. The aim of this paper is twofold. On the one hand, it describes the proof of concept that the channel underwent and its subsequent validation (with performances nearing 99% success rate). On the other hand, it defines a classification framework (I2MA) for post-metering value-added services, in order to categorize each use case based on both level of service and expected benefits, and understand its maturity level. As an example, we apply the methodology to the 16 use cases defined in Italy. The lessons learned from the regulatory, technological, and functional approach of the Italian experience bring us to the provision of recommendations for researchers and industry experts. In particular, we argue that a well-functioning post-metering value-added services' market can flourish when: i) distribution system operators certify the measurements coming from smart meters; ii) national regulatory authorities support the technological innovation needed for setting up this market; and iii) service providers create customer-oriented solutions based on smart meters' data.
Optimization of Electrolyte Rebalancing in Vanadium Redox Flow Batteries
Authors: Mehdi Jafari, Apurba Sakti, Audun Botterud
Date: 2021-07-07 00:00:00

This paper presents a novel algorithm to optimize energy capacity restoration of vanadium redox flow batteries (VRFBs). VRFB technologies can have their lives prolonged through a partially restoration of the lost capacity by electrolyte rebalancing. Our algorithm finds the optimal number and time of these rebalancing services to minimize the service cost, while maximizing the revenues from energy arbitrage. We show that the linearized form of this problem can be analytically solved, and that the objective function is convex. To solve the complete problem, we develop a two-step mixed integer linear programming (MILP) algorithm, which first finds the bounds for optimal number of services and then optimizes the number, and time of the services. We then present a theoretical analysis and optimization results for a case study of energy arbitrage in New York ISO.
A high performance active noise control system for magnetic fields
Authors: Tadas Pyragius, Kasper Jensen
Date: 2021-07-07 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We present a system for active noise control (ANC) of environmental magnetic fields based on a Filtered-x Least Mean Squares (FxLMS) algorithm. The system consists of a sensor that detects the ambient field noise and an error sensor that measures the signal of interest contaminated with the noise. These signals are fed to an adaptive algorithm that constructs a physical anti-noise signal cancelling the local magnetic field noise. The proposed system achieves a maximum of 35 dB root-mean-square (RMS) noise suppression in the DC-1 kHz band and 50 dB and 40 dB amplitude suppression of 50 Hz and 150 Hz AC line noise respectively for all three axial directions of the magnetic vector field.
Novel Approach To Synchronisation Of Wearable IMUs Based On Magnetometers
Authors: Andreas Spilz, Michael Munz
Date: 2021-07-07 00:00:00
Tasks: Motion Estimation

Synchronisation of wireless inertial measurement units in human movement analysis is often achieved using event-based synchronisation techniques. However, these techniques lack precise event generation and accuracy. An inaccurate synchronisation could lead to large errors in motion estimation and reconstruction and therefore wrong analysis outputs. We propose a novel event-based synchronisation technique based on a magnetic field, which allows sub-sample accuracy. A setup featuring Shimmer3 inertial measurement units is designed to test the approach. The proposed technique shows to be able to synchronise with a maximum offset of below 2.6~ms with sensors measuring at 100~Hz. Also, the results indicate a reliable event generation and detection. The investigated parameters suggest a required synchronisation time of eight seconds. Further research should investigate the temperature changes that the sensors are exposed to during human motion analysis and their influence on the internal time measurement of the sensors. In addition, the approach should be tested using inertial measurement units from different manufacturers to investigate an identified constant offset in the accuracy measurements.
A General Controller Scheme for Stabilization & Disturbance Rejection with Application to Non-Linear Systems and its Implementation on 2 DOF Helicopter
Authors: Justin Jacob, Navin Khaneja
Date: 2021-07-07 00:00:00

A general controller scheme for stabilizing a non-linear system, which has its origin from the linear system theory, is proposed in this paper. The proposed controller can stabilize the non-linear system subjected to initial conditions. An effective way to obtain the controller parameters is presented with the knowledge of the system model. The controller is designed for the linear time-invariant (LTI) system, which can reject any disturbance acting on it. Paper emphasis the idea of an integrator controller in disturbance rejection. The concept is extended to the application to non-linear systems where the non-linearities are assessed as the disturbance to the refined linear part of the system. Boundedness and convergence of the non-linear system with the controller are proved to justify system stabilization. Hardware implementation of the controller on the 2 dof helicopter model is presented with experimental results, which validates the proposed control scheme.
Dynamic Virtual Power Plant Design for Fast Frequency Reserves: Coordinating Hydro and Wind
Authors: Joakim Björk, Karl Henrik Johansson, Florian Dörfler
Date: 2021-07-07 00:00:00

To ensure frequency stability in future low-inertia power grids, fast ancillary services such as fast frequency reserves (FFR) have been proposed. In this work, the coordination of conventional (slow) frequency containment reserves (FCR) with FFR is treated as a decentralized model matching problem. The design results in a dynamic virtual power plant (DVPP) whose aggregated output fulfills the system operator (SO) requirements in all time scales, while accounting for the capacity and bandwidth limitation of participating devices. This is illustrated in a 5-machine representation of the Nordic synchronous grid. In the Nordic grid, stability issues and bandwidth limitations associated with non-minimum phase zeros of hydropower is a well-known problem. By simulating the disconnection of a 1400 MW importing dc link, it is shown that the proposed DVPP design allows for coordinating fast FFR from wind, with slow FCR from hydro, while respecting dynamic limitations of all participating devices. The SO requirements are fulfilled in a realistic low-inertia scenario without the need to install battery storage or to waste wind energy by curtailing the wind turbines.
Position Constrained, Adaptive Control of Robotic Manipulators without Velocity Measurements
Authors: Samet Gul, Erkan Zergeroglu, Enver Tatlicioglu
Date: 2021-07-07 00:00:00
Tasks: Position

This work presents the design and the corresponding stability analysis of a model based, joint position tracking error constrained, adaptive output feedback controller for robot manipulators. Specifically, provided that the initial joint position tracking error starts within a predefined region, the proposed controller algorithm ensures that the joint tracking error remains inside this region and asymptotically approaches to zero, despite the lack of joint velocity measurements and uncertainties associated with the system dynamics. The need for the joint velocity measurements are removed via the use of a surrogate filter formulation in conjunction with the use of desired model compensation. The stability and the convergence of the closed loop system are proved via a barrier Lyapunov function based argument. A simulation performed on a two-link robotic manipulator is provided in order to illustrate the feasibility and effectiveness of the proposed method.
Embedding digital chronotherapy into medical devices -- A canine validation for controlling status epilepticus through multi-scale rhythmic brain stimulation
Authors: Mayela Zamora, Sebastian Meller, Filip Kajin, James J Sermon, Robert Toth, Moaad Benjaber, Derk-Jan Dijk, Rafal Bogacz, Gregory A Worrell, Antonio Valentin, Benoit Duchet, Holger A Volk, Timothy Denison
Date: 2021-07-07 00:00:00
Tasks: Rhythm

Circadian and other physiological rhythms play a key role in both normal homeostasis and disease processes. Such is the case of circadian and infradian seizure patterns observed in epilepsy. In this paper we explore a new implantable stimulator that implements chronotherapy as a feedforward input to supplement both open-loop and closed-loop methods. This integrated algorithm allows for stimulation to be adjusted to the ultradian, circadian and infradian patterns observed in patients through slowly-varying temporal adjustments of stimulation and algorithm sub-components, while also enabling adaption of stimulation based on immediate physiological needs such as a breakthrough seizure or change of posture. Embedded physiological sensors in the stimulator can be used to refine the baseline stimulation circadian pattern as a "digital zeitgeber". This approach is tested on a canine with severe drug-resistant idiopathic generalized epilepsy exhibiting a diurnal pattern correlated with sleep-wake cycles. Prior to implantation, the canine's cluster seizures evolved to status epilepticus (SE) and required emergency pharmacological intervention. The cranially-mounted system was fully-implanted bilaterally into the centromedian nucleus of the thalamus. Using time-based modulation, thalamocortical rhythm-specific tuning of frequency parameters as well as fast-adaptive modes based on activity, the canine experienced no further SE events post-implant as of the time of writing (seven months). Importantly, no significant cluster seizures have been observed either, allowing the reduction of rescue medication. The use of chronotherapy as a feedforward signal to augment adaptive neurostimulators could prove a useful method in conditions where sensitivity to temporal patterns are characteristics of the disease state, providing a novel mechanism for tailoring a more patient-specific therapy approach.
A Discrete Simulation Optimization Approach Towards Calibration of an Agent-based Simulation Model of Hepatitis C Virus Transmission
Authors: Soham Das, Navonil Mustafee, Varun Ramamohan
Date: 2021-07-06 00:00:00

This study demonstrates the implementation of the stochastic ruler discrete simulation optimization method for calibrating an agent-based model (ABM) developed to simulate hepatitis C virus (HCV) transmission. The ABM simulates HCV transmission between agents interacting in multiple environments relevant for HCV transmission in the Indian context. Key outcomes of the ABM are HCV and injecting drug user (IDU) prevalences among the simulated cohort. Certain input parameters of the ABM need to be calibrated so that simulation outcomes attain values as close as possible to real-world HCV and IDU prevalences. We conceptualize the calibration process as a discrete simulation optimization problem by discretizing the calibration parameter ranges, defining an appropriate objective function, and then applying the stochastic ruler random search method to solve this problem. We also present a method that exploits the monotonic relationship between the simulation outcomes and calibration parameters to yield improved calibration solutions with lesser computational effort.
Toward Efficient Wide-Area Identification of Multiple Element Contingencies in Power Systems
Authors: Hao Huang, Zeyu Mao, Mohammad Rasoul Narimani, Katherine R. Davis
Date: 2021-02-16 00:00:00

Power system N-x contingency analysis has inherent challenges due to its combinatorial characteristic where outages grow exponentially with the increase of x and N. To address these challenges, this paper proposes a method that utilizes Line Outage Distribution Factors (LODFs) and group betweenness centrality to identify subsets of critical branches. The proposed LODF metrics are used to select the high-impact branches. Based on each selected branch, the approach constructs the subgraph with parameters of distance and search level, while using branches' LODF metrics as the weights. A key innovation of this work is the use of the distance and search level parameters, which allow the subgraph to identify the most coupled critical elements that may be far away from a selected branch. The proposed approach is validated using the 200- and 500-bus test cases, and results show that the proposed approach can identify multiple N-x contingencies that cause violations.
Real-time Power System Simulation with Hardware Devices through DNP3 in Cyber-Physical Testbed
Authors: Hao Huang, C. Matthew Davis, Katherine R. Davis
Date: 2021-01-15 00:00:00

Modern power grids are dependent on communication systems for data collection, visualization, and control. Distributed Network Protocol 3 (DNP3) is commonly used in supervisory control and data acquisition (SCADA) systems in power systems to allow control system software and hardware to communicate. To study the dependencies between communication network security, power system data collection, and industrial hardware, it is important to enable communication capabilities with real-time power system simulation. In this paper, we present the integration of new functionality of a power systems dynamic simulation package into our cyber-physical power system testbed that supports real-time power system data transfer using DNP3, demonstrated with an industrial real-time automation controller (RTAC). The usage and configuration of DNP3 with real-world equipment in to achieve power system monitoring and control of a large-scale synthetic electric grid via this DNP3 communication is presented. Then, an exemplar of DNP3 data collection and control is achieved in software and hardware using the 2000-bus Texas synthetic grid.
Pairwise Comparison Evolutionary Dynamics with Strategy-Dependent Revision Rates: Stability and Delta-Passivity (Expanded Version)
Authors: Semih Kara, Nuno C. Martins
Date: 2021-07-06 00:00:00

We report on new stability conditions for evolutionary dynamics in the context of population games. We adhere to the prevailing framework consisting of many agents, grouped into populations, that interact noncooperatively by selecting strategies with a favorable payoff. Each agent is repeatedly allowed to revise its strategy at a rate referred to as revision rate. Previous stability results considered either that the payoff mechanism was a memoryless potential game, or allowed for dynamics (in the payoff mechanism) at the expense of precluding any explicit dependence of the agents' revision rates on their current strategies. Allowing the dependence of revision rates on strategies is relevant because the agents' strategies at any point in time are generally unequal. To allow for strategy-dependent revision rates and payoff mechanisms that are dynamic (or memoryless games that are not potential), we focus on an evolutionary dynamics class obtained from a straightforward modification of one that stems from the so-called impartial pairwise comparison strategy revision protocol. Revision protocols consistent with the modified class retain from those in the original one the advantage that the agents operate in a fully decentralized manner and with minimal information requirements - they need to access only the payoff values (not the mechanism) of the available strategies. Our main results determine conditions under which system-theoretic passivity properties are assured, which we leverage for stability analysis.
Control Schemes For Distribution Grids With Mass Distributed Generation
Authors: Umair Shahzad
Date: 2021-07-02 00:00:00

This project discusses the control schemes for distribution grids with a large amount of wind penetration. Microgrids are constantly gaining popularity, especially in the countries, where there is energy crisis. Various systems, including synchronous generators, grid and loads, have been investigated in this project. Major focus is placed on active and reactive power sharing. Droop control for multiple synchronous generators is explored. The phenomenon of load transients has also been reviewed and associated simulations have been carried out on SimPower Systems. Constant wind power has been introduced and behaviour of the electrical system is observed. Behaviour of the system under variable wind has also been analysed. Moreover, recent development projects and previous works, regarding microgrids and distributed generation, have been discussed.
Predicting the diversity of early epidemic spread on networks
Authors: Andrea J. Allen, Mariah C. Boudreau, Nicholas J. Roberts, Antoine Allard, Laurent Hébert-Dufresne
Date: 2021-07-07 00:00:00
Tasks: Diversity

The interplay of biological, social, structural and random factors makes disease forecasting extraordinarily complex. The course of an epidemic exhibits average growth dynamics determined by features of the pathogen and the population, yet also features significant variability reflecting the stochastic nature of disease spread. In this work, we reframe a stochastic branching process analysis in terms of probability generating functions and compare it to continuous time epidemic simulations on networks. In doing so, we predict the diversity of emerging epidemic courses on both homogeneous and heterogeneous networks. We show how the challenge of inferring the early course of an epidemic falls on the randomness of disease spread more so than on the heterogeneity of contact patterns. We provide an analysis which helps quantify, in real time, the probability that an epidemic goes supercritical or conversely, dies stochastically. These probabilities are often assumed to be one and zero, respectively, if the basic reproduction number, or R0, is greater than 1, ignoring the heterogeneity and randomness inherent to disease spread. This framework can give more insight into early epidemic spread by weighting standard deterministic models with likelihood to inform pandemic preparedness with probabilistic forecasts.
Ecosystems are showing symptoms of resilience loss
Authors: Juan C. Rocha
Date: 2021-07-07 00:00:00
Tasks: Management

Ecosystems around the world are at risk of critical transitions due to increasing anthropogenic pressures and climate change. Yet it is unclear where the risks are higher or where in the world ecosystems are more vulnerable. Here I measure resilience of primary productivity proxies for marine and terrestrial ecosystems globally. Up to 29% of global terrestrial ecosystem, and 24% marine ones, show symptoms of resilience loss. These symptoms are shown in all biomes, but Arctic tundra and boreal forest are the most affected, as well as the Indian Ocean and Eastern Pacific. Although the results are likely an underestimation, they enable the identification of risk areas as well as the potential synchrony of some transitions, helping prioritize areas for management interventions and conservation.
Estimation and Inference in Factor Copula Models with Exogenous Covariates
Authors: Alexander Mayer, Dominik Wied
Date: 2021-07-07 00:00:00

A factor copula model is proposed in which factors are either simulable or estimable from exogenous information. Point estimation and inference are based on a simulated methods of moments (SMM) approach with non-overlapping simulation draws. Consistency and limiting normality of the estimator is established and the validity of bootstrap standard errors is shown. Doing so, previous results from the literature are verified under low-level conditions imposed on the individual components of the factor structure. Monte Carlo evidence confirms the accuracy of the asymptotic theory in finite samples and an empirical application illustrates the usefulness of the model to explain the cross-sectional dependence between stock returns.
Dynamic Ordered Panel Logit Models
Authors: Bo E. Honoré, Chris Muris, Martin Weidner
Date: 2021-07-07 00:00:00
Tasks: regression, valid

This paper studies a dynamic ordered logit model for panel data with fixed effects. The main contribution of the paper is to construct a set of valid moment conditions that are free of the fixed effects. The moment functions can be computed using four or more periods of data, and the paper presents sufficient conditions for the moment conditions to identify the common parameters of the model, namely the regression coefficients, the autoregressive parameters, and the threshold parameters. The availability of moment conditions suggests that these common parameters can be estimated using the generalized method of moments, and the paper documents the performance of this estimator using Monte Carlo simulations and an empirical illustration to self-reported health status using the British Household Panel Survey.