pwc-archive/papers-with-abstracts huggingface dataset:
Stability of Linear Set-Membership Filters With Respect to Initial Conditions: An Observation-Information Perspective
Authors: Yirui Cong, Xiangke Wang, Xiangyun Zhou
Date: 2022-03-26 00:00:00

The issue of filter stability with respect to (w.r.t.) the initial condition refers to the unreliable filtering process caused by improper prior information of the initial state. This paper focuses on analyzing and resolving the stability issue w.r.t. the initial condition of the classical Set-Membership Filters (SMFs) for linear time-invariance systems, which has not yet been well understood in the literature. To this end, we propose a new concept -- the Observation-Information Tower (OIT), which describes how the measurements affect the estimate in a set-intersection manner without relying on the initial condition. The proposed OIT enables a rigorous stability analysis, a new SMFing framework, as well as an efficient filtering algorithm. Specifically, based on the OIT, explicit necessary and sufficient conditions for stability w.r.t. the initial condition are provided for the classical SMFing framework. Furthermore, the OIT inspires a stability-guaranteed SMFing framework, which fully handles the stability issue w.r.t. the initial condition. Finally, with the OIT-inspired framework, we develop a fast and stable constrained zonotopic SMF, which significantly overcomes the wrapping effect.
Research on Flexibility Margin of Electric-Hydrogen Coupling Energy Block Based on Model Predictive Control
Authors: Zijiao Han, Shun Yuan, Yannan Dong, Shaohua Ma, Yudong Bian, Xinyu Mao
Date: 2022-03-26 00:00:00
Tasks: Model Predictive Control, Scheduling

Hydrogen energy plays an important role in the transformation of low-carbon energy, and electric hydrogen coupling will become a typical energy scenario. Aiming at the operation flexibility of low-carbon electricity hydrogen coupling system with high proportion of wind power and photovoltaic, this paper studies the flexibility margin of electricity hydrogen coupling energy block based on model predictive control (MPC). By analyzing the power exchange characteristics of heterogeneous energy, the homogenization models of various heterogeneous energy sources are established. According to the analysis of power system flexibility margin, three dimensions of flexibility margin evaluation indexes are defined from the dimension of system operation, and an electricity hydrogen coupling energy block scheduling model is established. The model predictive control algorithm is used to optimize the power balance operation of the electro hydrogen coupling energy block, and the flexibility margin of the energy block is quantitatively analyzed and calculated. Through the example analysis, it is verified that the calculation method proposed in this paper can not only realize the on-line power balance optimization of electric hydrogen coupling energy block, but also effectively quantify the operation flexibility margin of electric hydrogen coupling energy block.
A Risk-Driven Probabilistic Approach to Quantify Resilience in Power Distribution Systems
Authors: Abodh Poudyal, Anamika Dubey, Shiva Poudel
Date: 2022-03-25 00:00:00

It is of growing concern to ensure resilience in power distribution systems to extreme weather events. However, there are no clear methodologies or metrics available for resilience assessment that allows system planners to assess the impact of appropriate planning measures and new operational procedures for resilience enhancement. In this paper, we propose a resilience metric using parameters that define system attributes and performance. To represent extreme events (tail probability), the conditional value-at-risk of each of the parameters are combined using Choquet Integral to evaluate the overall resilience. The effectiveness of the proposed resilience metric is studied within the simulation-based framework under extreme weather scenarios with the help of a modified IEEE 123-bus system. With the proposed framework, system operators will have additional flexibility to prioritize one investment over the others to enhance the resilience of the grid.
Towards Stable Interstellar Flight: Levitation of a Laser-Propelled Sailcraft*
Authors: Afroza Shirin, Edl Schamiloglu, Rafael Fierro
Date: 2022-03-24 00:00:00

Exploring and traveling to distant stars has long fascinated humanity but has been limited due to the vast distances. The Breakthrough Starshot Program aims at eliminating this limitation by traveling to Alpha Centauri, which is 4.37 light-years away. Thus, it is only possible if a vehicle travels at a substantial fraction of the speed of light. The Breakthrough Starshot Program initiatives to develop a proof-of-concept that is accelerating a sail to relativistic speeds using a laser beam aimed at the sail. At this high speed, while a stable beam riding is one of the crucial concerns of this concept, the dynamic stability analysis of a sail is hardly present in the previous literature. Furthermore, it is important to investigate the dynamic stability in the experiment before driving the sail to relativistic speeds. As a proof of concept, we study the dynamic stability of the sail levitated at a certain height by a laser beam. The sail's dynamics are modeled as a rigid body whose shape is parameterized by a sweep function. We estimate the region of attraction (ROA) for dynamic stability analysis using Lyapunov theory and Sum-of-square (SOS) programming. The ROA confirms how much a levitated sail can tolerate the transverse and angular perturbations. We also conclude on some of the important parameters of the sail that affects the dynamic stability. Simulation results validate our theoretical analysis.
A Cellular Automaton Model for the generation of Brainwaves
Authors: Sean Knight, Navjot Gadda
Date: 2022-03-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

We describe a cellular-automaton based, two-dimensional (2D) lattice model which generates global oscillations in the EEG spectrum as well as time series of local field potentials resembling those observed during slow wave sleep. This is made possible by the presence of pacemakers (local oscillators) which can be spontaneously ignited and whose activity propagates through space to synchronize all connected nodes.
Cluster Algebras: Network Science and Machine Learning
Authors: Pierre-Philippe Dechant, Yang-Hui He, Elli Heyes, Edward Hirst
Date: 2022-03-25 00:00:00
Tasks: Graph Embedding

Cluster algebras have recently become an important player in mathematics and physics. In this work, we investigate them through the lens of modern data science, specifically with techniques from network science and machine learning. Network analysis methods are applied to the exchange graphs for cluster algebras of varying mutation types. The analysis indicates that when the graphs are represented without identifying by permutation equivalence between clusters an elegant symmetry emerges in the quiver exchange graph embedding. The ratio between number of seeds and number of quivers associated to this symmetry is computed for finite Dynkin type algebras up to rank 5, and conjectured for higher ranks. Simple machine learning techniques successfully learn to classify cluster algebras using the data of seeds. The learning performance exceeds 0.9 accuracies between algebras of the same mutation type and between types, as well as relative to artificially generated data.
Dynamic Structure in Four-strategy Game: Theory and Experiment
Authors: Zhijian Wang, Shujie Zhou, Qinmei Yao, Yijia, Wang
Date: 2022-03-28 00:00:00

Game dynamics theory, as a field of science, the consistency of theory and experiment is essential. In the past 10 years, important progress has been made in the merging of the theory and experiment in this field, in which dynamics cycle is the presentation. However, the merging works have not got rid of the constraints of Euclidean two-dimensional cycle so far. This paper uses a classic four-strategy game to study the dynamic structure (non-Euclidean superplane cycle). The consistency is in significant between the three ways: (1) the analytical results from evolutionary dynamics equations, (2) agent-based simulation results from learning models and (3) laboratory results from human subjects game experiments. The consistency suggests that, game dynamic structure could be quantitatively predictable, observable and controllable in general.
Molecular simulations matching denaturation experiments for N6-Methyladenosine
Authors: Valerio Piomponi, Thorben Fröhlking, Mattia Bernetti, Giovanni Bussi
Date: 2022-03-28 00:00:00

Post-transcriptional modifications are crucial for RNA function and can affect its structure and dynamics. Force-field based classical molecular dynamics simulations are a fundamental tool to characterize biomolecular dynamics and their application to RNA is flourishing. Here we show that the set of force-field parameters for N$^6$-methyladenosine (m$^6$A) developed for the commonly used AMBER force field does not reproduce duplex denaturation experiments and, specifically, cannot be used to describe both paired and unpaired states. Then we use reweighting techniques to derive new parameters matching available experimental data. The resulting force field can be used to properly describe paired and unpaired m$^6$A in both syn and anti conformation, and thus opens the way to the use of molecular simulations to investigate the effects of N6 methylations on RNA structural dynamics.
Ultrafast Ultrasound Imaging for 3D Shear Wave Absolute Vibro-Elastography
Authors: Hoda S. Hashemi, Reza Zahiri Azar, Septimiu E. Salcudean, Robert N. Rohling
Date: 2022-03-26 00:00:00

Shear wave absolute vibro-elastography (S-WAVE) is an imaging technique that generates steady-state shear waves inside the tissue using multi-frequency excitation from an external vibration source. In this work, plane wave imaging is introduced to reduce total acquisition time while retaining the benefit of 3D formulation. Plane wave imaging with a frame rate of 3000 frames/s is followed by 3D absolute elasticity estimation. We design two imaging sequences of ultrafast S-WAVE for two sets of excitation frequencies using a Verasonics system and a motorized swept ultrasound transducer to synchronize ultrasound acquisition with the external mechanical excitation. The overall data collection time is improved by 83-88% compared to the original 3D S-WAVE because of the per-channel acquisition offered by the Verasonics system. Tests are performed on liver fibrosis tissue-mimicking phantoms and on ex vivo bovine liver. The curl operator was previously used in magnetic resonance elastography (MRE) to cancel out the effect of the compressional waves. In this work, we apply the curl operator to the full 3D displacement field followed by 3D elasticity reconstruction. The results of phantom experiment show more accurate elasticity estimation as well as 18% less standard deviation (STD) compared to reconstruction using the curl of a 2D displacement field and 45% less STD than without the curl. We also compare our experimental results with a previous method based on acoustic radiation force impulse (ARFI) and achieve closer results to phantom manufacturer provided values with ultrafast S-WAVE. Furthermore, the dependency of the bovine liver elasticity on the frequency of excitation was also shown with our system.
Inference in Difference-in-Differences: How Much Should We Trust in Independent Clusters?
Authors: Bruno Ferman
Date: 2019-09-04 00:00:00

We analyze the challenges for inference in difference-in-differences (DID) when there is spatial correlation. We present novel theoretical insights and empirical evidence on the settings in which ignoring spatial correlation should lead to more or less distortions in DID applications. We show that details such as the time frame used in the estimation, the choice of the treated and control groups, and the choice of the estimator, are key determinants of distortions due to spatial correlation. We also analyze the feasibility and trade-offs involved in a series of alternatives to take spatial correlation into account. Given that, we provide relevant recommendations for applied researchers on how to mitigate and assess the possibility of inference distortions due to spatial correlation.
Racial Sentencing Disparities and Differential Progression Through the Criminal Justice System: Evidence From Linked Federal and State Court Data
Authors: Brendon McConnell
Date: 2022-03-27 00:00:00
Tasks: Selection bias

Several key actors -- police, prosecutors, judges -- can alter the course of individuals passing through the multi-staged criminal justice system. I use linked arrest-sentencing data for federal courts from 1994-2010 to examine the role that earlier stages play when estimating Black-white sentencing gaps. I find no evidence of sample selection at play in the federal setting, suggesting federal judges are largely responsible for racial sentencing disparities. In contrast, I document substantial sample selection bias in two different state courts systems. Estimates of racial and ethnic sentencing gaps that ignore selection underestimate the true disparities by 15% and 13% respectively.
A Network Approach to Consumption
Authors: Jan Schulz, Daniel M. Mayerhoffer
Date: 2022-03-27 00:00:00

The nexus between debt and inequality has attracted considerable scholarly attention in the wake of the global financial crisis. One prominent candidate to explain the striking co-evolution of income inequality and private debt in this period has been the theory of upward-looking consumption externalities leading to expenditure cascades. We propose a parsimonious model of upward-looking consumption at the micro level mediated by perception networks with empirically plausible topologies. This allows us to make sense of the ambiguous empirical literature on the relevance of this channel. Up to our knowledge, our approach is the first to make the reference group to which conspicuous consumption relates explicit. Our model, based purely on current income, replicates the major stylised facts regarding micro consumption behaviour and is thus observationally equivalent to the workhorse permanent income hypothesis, without facing its dual problem of `excess smoothness' and `excess sensitivity'. We also demonstrate that the network topology and segregation has a significant effect on consumption patterns which has so far been neglected.
A Network-Based Explanation of Inequality Perceptions
Authors: Jan Schulz, Daniel M. Mayerhoffer, Anna Gebhard
Date: 2022-03-27 00:00:00

Across income groups and countries, individual citizens perceive economic inequality spectacularly wrong. These misperceptions have far-reaching consequences, as it is perceived inequality, not actualinequality informing redistributive preferences. The prevalence of this phenomenon is independent of social class and welfare regime, which suggests the existence of a common mechanism behind public perceptions. The literature has identified several stylised facts on how individual perceptions respond to actual inequality and how these biases vary systematically along the income distribution. We propose a network-based explanation of perceived inequality building on recent advances in random geometric graph theory. The generating mechanism can replicate all of forementioned stylised facts simultaneously. It also produces social networks that exhibit salient features of real-world networks; namely, they cannot be statistically distinguished from small-world networks, testifying to the robustness of our approach. Our results, therefore, suggest that homophilic segregation is a promising candidate to explain inequality perceptions with strong implications for theories of consumption and voting behaviour.
Why Do Banks Find Business Process Compliance So Challenging? An Australian Case Study
Authors: Nigel Adams, Adriano Augusto, Michael Davern, Marcello La Rosa
Date: 2022-03-24 00:00:00

Banks play an intrinsic role in any modern economy, recycling capital from savers to borrowers. They are heavily regulated and there have been a significant number of well publicized compliance failings in recent years. This is despite Business Process Compliance (BPC) being both a well researched domain in academia and one where significant progress has been made. This study seeks to determine why Australian banks find BPC so challenging. We interviewed 22 senior managers from a range of functions within the four major Australian banks to identify the key challenges. Not every process in every bank is facing the same issues, but in processes where a bank is particularly challenged to meet its compliance requirements, the same themes emerge. The compliance requirement load they bear is excessive, dynamic and complex. Fulfilling these requirements relies on impenetrable spaghetti processes, and the case for sustainable change remains elusive, locking banks into a fail-fix cycle that increases the underlying complexity. This paper proposes a conceptual framework that identifies and aggregates the challenges, and a circuit-breaker approach as an "off ramp" to the fail-fix cycle.
Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach
Authors: Isaiah Hull, Anna Grodecka-Messi
Date: 2022-03-23 00:00:00

How do property prices respond to changes in local taxes and local public services? Attempts to measure this, starting with Oates (1969), have suffered from a lack of local public service controls. Recent work attempts to overcome such data limitations through the use of quasi-experimental methods. We revisit this fundamental problem, but adopt a different empirical strategy that pairs the double machine learning estimator of Chernozhukov et al. (2018) with a novel dataset of 947 time-varying local characteristic and public service controls for all municipalities in Sweden over the 2010-2016 period. We find that properly controlling for local public service and characteristic controls more than doubles the estimated impact of local income taxes on house prices. We also exploit the unique features of our dataset to demonstrate that tax capitalization is stronger in areas with greater municipal competition, providing support for a core implication of the Tiebout hypothesis. Finally, we measure the impact of public services, education, and crime on house prices and the effect of local taxes on migration.
SASV 2022: The First Spoofing-Aware Speaker Verification Challenge
Authors: Jee-weon Jung, Hemlata Tak, Hye-jin Shim, Hee-Soo Heo, Bong-Jin Lee, Soo-Whan Chung, Ha-Jin Yu, Nicholas Evans, Tomi Kinnunen
Date: 2022-03-28 00:00:00
Tasks: Speaker Verification

The first spoofing-aware speaker verification (SASV) challenge aims to integrate research efforts in speaker verification and anti-spoofing. We extend the speaker verification scenario by introducing spoofed trials to the usual set of target and impostor trials. In contrast to the established ASVspoof challenge where the focus is upon separate, independently optimised spoofing detection and speaker verification sub-systems, SASV targets the development of integrated and jointly optimised solutions. Pre-trained spoofing detection and speaker verification models are provided as open source and are used in two baseline SASV solutions. Both models and baselines are freely available to participants and can be used to develop back-end fusion approaches or end-to-end solutions. Using the provided common evaluation protocol, 23 teams submitted SASV solutions. When assessed with target, bona fide non-target and spoofed non-target trials, the top-performing system reduces the equal error rate of a conventional speaker verification system from 23.83% to 0.13%. SASV challenge results are a testament to the reliability of today's state-of-the-art approaches to spoofing detection and speaker verification.
Analysis of Voice Conversion and Code-Switching Synthesis Using VQ-VAE
Authors: Shuvayanti Das, Jennifer Williams, Catherine Lai
Date: 2022-03-28 00:00:00
Tasks: Speech Synthesis, Voice Conversion

This paper presents an analysis of speech synthesis quality achieved by simultaneously performing voice conversion and language code-switching using multilingual VQ-VAE speech synthesis in German, French, English and Italian. In this paper, we utilize VQ code indices representing phone information from VQ-VAE to perform code-switching and a VQ speaker code to perform voice conversion in a single system with a neural vocoder. Our analysis examines several aspects of code-switching including the number of language switches and the number of words involved in each switch. We found that speech synthesis quality degrades after increasing the number of language switches within an utterance and decreasing the number of words. We also found some evidence of accent transfer when performing voice conversion across languages as observed when a speaker's original language differs from the language of a synthetic target utterance. We present results from our listening tests and discuss the inherent difficulties of assessing accent transfer in speech synthesis. Our work highlights some of the limitations and strengths of using a semi-supervised end-to-end system like VQ-VAE for handling multilingual synthesis. Our work provides insight into why multilingual speech synthesis is challenging and we suggest some directions for expanding work in this area.
On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition
Authors: Mengzhe Geng, Xurong Xie, Rongfeng Su, Jianwei Yu, Zengrui Jin, Tianzi Wang, Shujie Hu, Zi Ye, Helen Meng, Xunying Liu
Date: 2022-03-28 00:00:00
Tasks: Diversity, speech-recognition, Speech Recognition

Accurate recognition of dysarthric and elderly speech remain challenging tasks to date. Speaker-level heterogeneity attributed to accent or gender, when aggregated with age and speech impairment, create large diversity among these speakers. Scarcity of speaker-level data limits the practical use of data-intensive model based speaker adaptation methods. To this end, this paper proposes two novel forms of data-efficient, feature-based on-the-fly speaker adaptation methods: variance-regularized spectral basis embedding (SVR) and spectral feature driven f-LHUC transforms. Experiments conducted on UASpeech dysarthric and DementiaBank Pitt elderly speech corpora suggest the proposed on-the-fly speaker adaptation approaches consistently outperform baseline iVector adapted hybrid DNN/TDNN and E2E Conformer systems by statistically significant WER reduction of 2.48%-2.85% absolute (7.92%-8.06% relative), and offline model based LHUC adaptation by 1.82% absolute (5.63% relative) respectively.
An Effective Dereverberation Algorithm by Fusing MVDR and MCLP
Authors: Fengqi Tan, Changchun Bao
Date: 2022-03-28 00:00:00
Tasks: Blocking

In the scenario with reverberation, the experience of human-machine interaction will become worse. In order to solve this problem, many methods for the dereverberation have emerged. At present, how to update the parameters of the Kalman filter in the existing dereverberation methods based on multichannel linear prediction (MCLP) is a challenging task, especially, accurate power spectral density (PSD) estimation of target speech. In this paper, minimum variance distortionless response (MVDR) beamformer and MCLP are effectively fused in the dereverberation, where the PSD of target speech used for Kalman filter is modified in the MCLP. In order to construct a MVDR beamformer, the PSD of late reverberation and the PSD of the noise are estimated simultaneously by the blocking-based PSD estimator. Thus, the PSD of target speech used for Kalman filter can be obtained by subtracting the PSD of late reverberation and the PSD of the noise from the PSD of observation signal. Compared to the reference methods, the proposed method shows an outstanding performance.