pwc-archive/papers-with-abstracts huggingface dataset:
Variable-Depth Simulation of Most Permissive Boolean Networks
Authors: Théo Roncalli, Loïc Paulevé
Date: 2022-06-25 00:00:00

In systems biology, Boolean networks (BNs) aim at modeling the qualitative dynamics of quantitative biological systems. Contrary to their (a)synchronous interpretations, the Most Permissive (MP) interpretation guarantees capturing all the trajectories of any quantitative system compatible with the BN, without additional parameters. Notably, the MP mode has the ability to capture transitions related to the heterogeneity of time scales and concentration scales in the abstracted quantitative system and which are not captured by asynchronous modes. So far, the analysis of MPBNs has focused on Boolean dynamical properties, such as the existence of particular trajectories or attractors. This paper addresses the sampling of trajectories from MPBNs in order to quantify the propensities of attractors reachable from a given initial BN configuration. The computation of MP transitions from a configuration is performed by iteratively discovering possible state changes. The number of iterations is referred to as the permissive depth, where the first depth corresponds to the asynchronous transitions. This permissive depth reflects the potential concentration and time scales heterogeneity along the abstracted quantitative process. The simulation of MPBNs is illustrated on several models from the literature, on which the depth parametrization can help to assess the robustness of predictions on attractor propensities changes triggered by model perturbations.
Impedance-based AC/DC Terminal Modeling and Analysis of MMC-BTB system
Authors: Chongbin Zhao, Qirong Jiang
Date: 2022-06-25 00:00:00

Impedance-based small-signal stability analysis is widely applied in practical engineering with modular multilevel converters (MMCs). However, the deficiencies of existing impedance models (IMs) and the idealized extension for the single MMC influence the analyses in multiterminal systems. Such gaps are filled by focusing on an MMC-based back-to-back system in this paper. To obtain the steady-state trajectory of the system, a numerical method is first proposed based on Newton-Raphson iteration in the frequency domain. Then, by substituting the shared terminal dynamics with active or passive devices, theoretical AC/DC IMs that consider typical control loops with the pure time delay, are directly established based on the multiharmonic linearization. Further aided by the derived IMs, two neglected aspects in the current literature, i.e., the influence of power transformers on low-frequency impedance characteristics and the rationality of using simplified IMs for high-frequency resonance studies, are investigated. It is confirmed that the stability of interlinking systems should be comprehensively analyzed at both AC and DC terminals. This helps position the instability source, obtain the stability margin, and guide the supplementary control strategy. All IMs and analyses are verified by frequency scans and simulations in PSCAD.
Adaptive Neural Network Stochastic-Filter-based Controller for Attitude Tracking with Disturbance Rejection
Authors: Hashim A. Hashim, Kyriakos G. Vamvoudakis
Date: 2022-06-24 00:00:00

This paper proposes a real-time neural network (NN) stochastic filter-based controller on the Lie Group of the Special Orthogonal Group $SO(3)$ as a novel approach to the attitude tracking problem. The introduced solution consists of two parts: a filter and a controller. Firstly, an adaptive NN-based stochastic filter is proposed that estimates attitude components and dynamics using measurements supplied by onboard sensors directly. The filter design accounts for measurement uncertainties inherent to the attitude dynamics, namely unknown bias and noise corrupting angular velocity measurements. The closed loop signals of the proposed NN-based stochastic filter have been shown to be semi-globally uniformly ultimately bounded (SGUUB). Secondly, a novel control law on $SO(3)$ coupled with the proposed estimator is presented. The control law addresses unknown disturbances. In addition, the closed loop signals of the proposed filter-based controller have been shown to be SGUUB. The proposed approach offers robust tracking performance by supplying the required control signal given data extracted from low-cost inertial measurement units. While the filter-based controller is presented in continuous form, the discrete implementation is also presented. Additionally, the unit-quaternion form of the proposed approach is given. The effectiveness and robustness of the proposed filter-based controller is demonstrated using its discrete form and considering low sampling rate, high initialization error, high-level of measurement uncertainties, and unknown disturbances. Keywords: Neuro-adaptive, estimator, filter, observer, control system, trajectory tracking, Lyapunov stability, stochastic differential equations, nonlinear filter, attitude tracking control, observer-based controller.
Fast and Optimal Adaptive Tracking Control: A Novel Meta-Reinforcement Learning via Conditional Generative Adversarial Net
Authors: Mohammad Mahmoudi, Nasser Sadati
Date: 2022-06-24 00:00:00
Tasks: Meta Reinforcement Learning

The control of nonlinear systems with unknown dynamics has been a significant field of research for many years. This paper presents a novel data-driven optimal adaptive control structure with less control effort and faster adaptation than standard adaptive control counterparts. The proposed control structure utilizes the system's recorded data to increase the speed of adaptation and performance dramatically. In this study, we employ a conditional generative adversarial net (CGAN) as a novel central pattern generator to reproduce the steady-state harmonic pattern of the control signals matching the system's uncertainties over a wide range. We can also use the CGAN architecture as a fault detector. The CGAN provides a low-dimensional latent space of uncertainties. It enables rapid and convenient adaptation when there are many parametric uncertainties, especially for large-scale systems. Then, we introduce a novel meta-reinforcement learning framework to adapt the latent space of CGAN to the system's uncertainties as an optimal direct adaptive controller without any system identifier. Another part of the control structure is a regulator that achieves semi-global asymptotic tracking using the Lyapunov stability analysis. Finally, via some simulations, we evaluate the capabilities of the proposed designs on two dynamical systems, a robot manipulator and a large-scale musculoskeletal structure, in the presence of disturbance and perturbation.
Undetectable GPS-Spoofing Attack on Time Series Phasor Measurement Unit Data
Authors: Imtiaj Khan, Virgilio Centeno
Date: 2022-06-24 00:00:00
Tasks: Time Series, Time Series Analysis

The Phasor Measurement Unit (PMU) is an important metering device for smart grid. Like any other Intelligent Electronic Device (IED), PMUs are prone to various types of cyberattacks. However, one form of attack is unique to the PMU, the GPS-spoofing attack, where the time and /or the one second pulse (1 PPS) that enables time synchronization are modified and the measurements are computed using the modified time reference. This article exploits the vulnerability of PMUs in their GPS time synchronization signal. At first, the paper proposes an undetectable gradual GPS-spoofing attack with small incremental angle deviation over time. The angle deviation changes power flow calculation through the branches of the grids, without alerting the System Operator (SO) during off-peak hour. The attacker keeps instigating slow incremental variation in power flow calculation caused by GPS-spoofing relentlessly over a long period of time, with a goal of causing the power flow calculation breach the MVA limit of the branch at peak-hour. The attack is applied by solving a convex optimization criterion at regular time interval, so that after a specific time period the attack vector incurs a significant change in the angle measurements transmitted by the PMU. Secondly, while the attack modifies the angle measurements with GPS-spoofing attack, it ensures the undetectibility of phase angle variation by keeping the attack vector less than attack detection threshold. The proposed attack model is tested with Weighted Least Squared Error (WLSE), Kalman Filtering, and Hankel-matrix based GPS-spoofing attack detection models. Finally, we have proposed a gradient of low-rank approximation of Hankel-matrix based detection method to detect such relentless small incremental GPS-spoofing attack.
Modelling the Spread of SARS-CoV2 and its variants. Comparison with Real Data. Relations that have to be Satisfied to Achieve the Total Regression of the SARS-CoV2 Infection
Authors: Giorgio Sonnino, Philippe Peeters, Pasquale Nardone
Date: 2022-06-25 00:00:00

This work provides an overview on deterministic and stochastic models that have previously been proposed by us to study the transmission dynamics of the Coronavirus Disease 2019 (COVID-19) in Europe and USA. Briefly, we describe realistic deterministic and stochastic models for the evolution of the COVID-19 pandemic, subject to the lockdown and quarantine measures, which take into account the time-delay for recovery or death processes. Realistic dynamic equations for the entire process have been derived by adopting the so-called "kinetic-type reactions approach". The lockdown and the quarantine measures are modelled by some kind of inhibitor reactions where susceptible and infected individuals can be "trapped" into inactive states. The dynamics for the recovered people is obtained by accounting people who are only traced back to hospitalised infected people. To model the role of the Hospitals we take inspiration from the Michaelis-Menten's enzyme-substrate reaction model (the so-called "MM reaction") where the "enzyme" is associated to the "available hospital beds", the "substrate" to the "infected people", and the "product" to the "recovered people", respectively. The statistical properties of the models, in particular the relevant correlation functions and the probability density functions, have duly been evaluated. We validate our theoretical predictions with a large series of experimental data for Italy, Germany, France, Belgium and United States, and we also compare data for Italy and Belgium with the theoretical predictions of the logistic model. We found that our predictions are in good agreement with the real world since the onset of COVID 19, contrary to the the logistics model that only applies in the first days of the pandemic. In the final part of the work, we can find the (theoretical) relationships that should be satisfied to obtain the disappearance of the virus.
Instrumented Common Confounding
Authors: Christian Tien
Date: 2022-06-26 00:00:00
Tasks: Causal Inference

Causal inference is difficult in the presence of unobserved confounders. We introduce the instrumented common confounding (ICC) approach to (nonparametrically) identify causal effects with instruments, which are exogenous only conditional on some unobserved common confounders. The ICC approach is most useful in rich observational data with multiple sources of unobserved confounding, where instruments are at most exogenous conditional on some unobserved common confounders. Suitable examples of this setting are various identification problems in the social sciences, nonlinear dynamic panels, and problems with multiple endogenous confounders. The ICC identifying assumptions are closely related to those in mixture models, negative control and IV. Compared to mixture models [Bonhomme et al., 2016], we require less conditionally independent variables and do not need to model the unobserved confounder. Compared to negative control [Cui et al., 2020], we allow for non-common confounders, with respect to which the instruments are exogenous. Compared to IV [Newey and Powell, 2003], we allow instruments to be exogenous conditional on some unobserved common confounders, for which a set of relevant observed variables exists. We prove point identification with outcome model and alternatively first stage restrictions. We provide a practical step-by-step guide to the ICC model assumptions and present the causal effect of education on income as a motivating example.
Travel time reliability in transportation networks: A review of methodological developments
Authors: Zhaoqi Zang, Xiangdong Xu, Kai Qu, Ruiya Chen, Anthony Chen
Date: 2022-06-25 00:00:00
Tasks: Philosophy

The unavoidable travel time variability in transportation networks, resulted from the widespread supply side and demand side uncertainties, makes travel time reliability (TTR) be a common and core interest of all the stakeholders in transportation systems, including planners, travelers, service providers, and managers. This common and core interest stimulates extensive studies on modeling TTR. Researchers have developed a range of theories and models of TTR, many of which have been incorporated into transportation models, policies, and project appraisals. Adopting the network perspective, this paper aims to provide an integrated framework for reviewing the methodological developments of modeling TTR in transportation networks, including its characterization, evaluation and valuation, and traffic assignment. Specifically, the TTR characterization provides a whole picture of travel time distribution in transportation networks. TTR evaluation and TTR valuation (known as the value of reliability, VOR) simply and intuitively interpret abstract characterized TTR to be well understood by different stakeholders of transportation systems. TTR-based traffic assignment investigates the effects of TTR on the individual users travel behavior and consequently the collective network flow pattern. As the above three topics are mainly separately studied in different disciplines and research areas, the integrated framework allows us to better understand their relationships and may contribute to developing possible combinations of TTR modeling philosophy. Also, the network perspective enables to focus on common challenges of modeling TTR, especially the uncertainty propagation from the uncertainty sources to the TTR at spatial levels including link, route, and the entire network. Some directions for future research are discussed in the era of new data environment, applications, and emerging technologies.
Urban Spatial Structure and the Potential for Vehicle Miles Traveled Reduction: The Effects of Accessibility to Jobs within and beyond Employment Sub-centers
Authors: Marlon G. Boarnet, Xize Wang
Date: 2022-06-25 00:00:00

This research examines the relationship between urban polycentric spatial structure and driving. We identified 46 employment sub-centers in the Los Angeles Combined Statistical Area and calculated access to jobs that are within and beyond these sub-centers. To address potential endogeneity problems, we use access to historically important places and transportation infrastructure in the early 20th century as instrumental variables for job accessibility indices. Our Two-stage Tobit models show that access to jobs is negatively associated with household vehicle miles traveled in this region. Among various accessibility measures, access to jobs outside sub-centers has the largest elasticity (-0.155). We examine the location of places in the top quintile of access to non-centered jobs and find that those locations are often inner ring suburban developments, near the core of the urban area and not far from sub-centers, suggesting that strategies of infill development that fill in the gaps between sub-centers, rather than focusing on already accessible downtowns and large sub-centers, may be the best land use approach to reduce VMT.
Can New Light Rail Reduce Personal Vehicle Carbon Emissions? A Before-After, Experimental-Control Evaluation in Los Angeles
Authors: Marlon G. Boarnet, Xize Wang, Douglas Houston
Date: 2022-06-25 00:00:00

This paper uses a before-after, experimental-control group method to evaluate the impacts of the newly opened Expo light rail transit line in Los Angeles on personal vehicle greenhouse gas (GHG) emissions. We applied the California Air Resources Board's EMFAC 2011 emission model to estimate the amount of daily average CO2 emissions from personal vehicle travel for 160 households across two waves, before and after the light rail opened. The 160 households were part of an experimental-ccontrol group research design. Approximately half of the households live within a half-mile of new Expo light rail stations (the experimental group) and the balance of the sampled households live beyond a half-mile from Expo light rail stations (the control group). Households tracked odometer mileage for all household vehicles for seven days in two sample waves, before the Expo Line opened (fall, 2011) and after the Expo Line opened (fall, 2012). Our analysis indicates that opening the Expo Line had a statistically significant impact on average daily CO2 emissions from motor vehicles. We found that the CO2 emission of households who reside within a half-mile of an Expo Line station was 27.17 percent smaller than those living more than a half-mile from a station after the opening of the light rail, while no significant difference exists before the opening. A difference-in-difference model suggests that the opening of the Expo Line is associated with 3,145 g less of household vehicle CO2 emissions per day as a treatment effect. A sensitivity analysis indicates that the emission reduction effect is also present when the experimental group of households is redefined to be those living within a kilometer from the new light rail stations.
False Narratives and Political Mobilization
Authors: Kfir Eliaz, Simone Galperti, Ran Spiegler
Date: 2022-06-25 00:00:00
Tasks: Attribute

We present an equilibrium model of politics in which political platforms compete over public opinion. A platform consists of a policy, a coalition of social groups with diverse intrinsic attitudes to policies, and a narrative. We conceptualize narratives as subjective models that attribute a commonly valued outcome to (potentially spurious) postulated causes. When quantified against empirical observations, these models generate a shared belief among coalition members over the outcome as a function of its postulated causes. The intensity of this belief and the members' intrinsic attitudes to the policy determine the strength of the coalition's mobilization. Only platforms that generate maximal mobilization prevail in equilibrium. Our equilibrium characterization demonstrates how false narratives can be detrimental for the common good, and how political fragmentation leads to their proliferation. The false narratives that emerge in equilibrium attribute good outcomes to the exclusion of social groups from ruling coalitions.
Unsupervised Voice Activity Detection by Modeling Source and System Information using Zero Frequency Filtering
Authors: Eklavya Sarkar, RaviShankar Prasad, Mathew Magimai. -Doss
Date: 2022-06-27 00:00:00
Tasks: Action Detection, Activity Detection

Voice activity detection (VAD) is an important pre-processing step for speech technology applications. The task consists of deriving segment boundaries of audio signals which contain voicing information. In recent years, it has been shown that voice source and vocal tract system information can be extracted using zero-frequency filtering (ZFF) without making any explicit model assumptions about the speech signal. This paper investigates the potential of zero-frequency filtering for jointly modeling voice source and vocal tract system information, and proposes two approaches for VAD. The first approach demarcates voiced regions using a composite signal composed of different zero-frequency filtered signals. The second approach feeds the composite signal as input to the rVAD algorithm. These approaches are compared with other supervised and unsupervised VAD methods in the literature, and are evaluated on the Aurora-2 database, across a range of SNRs (20 to -5 dB). Our studies show that the proposed ZFF-based methods perform comparable to state-of-art VAD methods and are more invariant to added degradation and different channel characteristics.
Unsupervised Instance Discriminative Learning for Depression Detection from Speech Signals
Authors: Jinhan Wang, Vijay Ravi, Jonathan Flint, Abeer Alwan
Date: 2022-06-27 00:00:00
Tasks: Data Augmentation, Depression Detection, Unsupervised Pre-training

Major Depressive Disorder (MDD) is a severe illness that affects millions of people, and it is critical to diagnose this disorder as early as possible. Detecting depression from voice signals can be of great help to physicians and can be done without any invasive procedure. Since relevant labelled data are scarce, we propose a modified Instance Discriminative Learning (IDL) method, an unsupervised pre-training technique, to extract augment-invariant and instance-spread-out embeddings. In terms of learning augment-invariant embeddings, various data augmentation methods for speech are investigated, and time-masking yields the best performance. To learn instance-spread-out embeddings, we explore methods for sampling instances for a training batch (distinct speaker-based and random sampling). It is found that the distinct speaker-based sampling provides better performance than the random one, and we hypothesize that this result is because relevant speaker information is preserved in the embedding. Additionally, we propose a novel sampling strategy, Pseudo Instance-based Sampling (PIS), based on clustering algorithms, to enhance spread-out characteristics of the embeddings. Experiments are conducted with DepAudioNet on DAIC-WOZ (English) and CONVERGE (Mandarin) datasets, and statistically significant improvements, with p-value 0.0015 and 0.05, respectively, are observed using PIS in the detection of MDD relative to the baseline without pre-training.
Optimized Decoding-Energy-Aware Encoding in Practical VVC Implementations
Authors: Matthias Kränzler, Adam Wieckowski, Geetha Ramasubbu, Benjamin Bross, André Kaup, Detlev Marpe, Christian Herglotz
Date: 2022-06-27 00:00:00

The optimization of the energy demand is crucial for modern video codecs. Previous studies show that the energy demand of VVC decoders can be improved by more than 50% if specific coding tools are disabled in the encoder. However, those approaches increase the bit rate by over 20% if the concept is applied to practical encoder implementations such as VVenC. Therefore, in this work, we investigate VVenC and study possibilities to reduce the additional bit rate, while still achieving low-energy decoding at reasonable encoding times. We show that encoding using our proposed coding tool profiles, the decoding energy efficiency is improved by over 25% with a bit rate increase of less than 5% with respect to standard encoding. Furthermore, we propose a second coding tool profile targeting maximum energy savings, which achieves 34% of energy savings at bitrate increases below 15%.