pwc-archive/papers-with-abstracts huggingface dataset:
Computationally efficient robust MPC using optimized constraint tightening
Authors: Anilkumar Parsi, Panagiotis Anagnostaras, Andrea Iannelli, Roy S. Smith
Date: 2022-04-05 00:00:00
Tasks: Model Predictive Control

A robust model predictive control (MPC) method is presented for linear, time-invariant systems affected by bounded additive disturbances. The main contribution is the offline design of a disturbance-affine feedback gain whereby the resulting constraint tightening is minimized. This is achieved by formulating the constraint tightening problem as a convex optimization problem with the feedback term as a variable. The resulting MPC controller has the computational complexity of nominal MPC, and guarantees recursive feasibility, stability and constraint satisfaction. The advantages of the proposed approach compared to existing robust MPC methods are demonstrated using numerical examples.
Scalable tube model predictive control of uncertain linear systems using ellipsoidal sets
Authors: Anilkumar Parsi, Andrea Iannelli, Roy S. Smith
Date: 2022-04-05 00:00:00
Tasks: Model Predictive Control

This work proposes a novel robust model predictive control (MPC) algorithm for linear systems affected by dynamic model uncertainty and exogenous disturbances. The uncertainty is modeled using a linear fractional perturbation structure with a time-varying perturbation matrix, enabling the algorithm to be applied to a large model class. The MPC controller constructs a state tube as a sequence of parameterized ellipsoidal sets to bound the state trajectories of the system. The proposed approach results in a semidefinite program to be solved online, whose size scales linearly with the order of the system. The design of the state tube is formulated as an offline optimization problem, which offers flexibility to impose desirable features such as robust invariance on the terminal set. This contrasts with most existing tube MPC strategies using polytopic sets in the state tube, which are difficult to design and whose complexity grows combinatorially with the system order. The algorithm guarantees constraint satisfaction, recursive feasibility, and stability of the closed loop. The advantages of the algorithm are demonstrated using two simulation studies.
Scalable global state synchronization of discrete-time double integrator multi-agent systems with input saturation via linear protocol (Completed Version)
Authors: Zhenwei Liu, Ali Saberi, Anton A. Stoorvogel
Date: 2022-04-05 00:00:00

This paper studies scalable global state synchronization of discrete-time double integrator multi-agent systems in presence of input saturation based on localized information exchange. A scale-free collaborative linear dynamic protocols design methodology is developed for discrete-time multi-agent systems with both full and partial-state couplings. And the protocol design methodology does not need any knowledge of the directed network topology and the spectrum of the associated Laplacian matrix. Meanwhile, the protocols are parametric based on a parameter set in which the designed protocols can guarantee the global synchronization result. Furthermore, the proposed protocol is scalable and achieves synchronization for any arbitrary number of agents.
Parameter Filter-based Event-triggered Learning
Authors: Sebastian Schlor, Friedrich Solowjow, Sebastian Trimpe
Date: 2022-04-05 00:00:00
Tasks: Active Learning, Model Predictive Control

Model-based algorithms are deeply rooted in modern control and systems theory. However, they usually come with a critical assumption - access to an accurate model of the system. In practice, models are far from perfect. Even precisely tuned estimates of unknown parameters will deteriorate over time. Therefore, it is essential to detect the change to avoid suboptimal or even dangerous behavior of a control system. We propose to combine statistical tests with dedicated parameter filters that track unknown system parameters from state data. These filters yield point estimates of the unknown parameters and, further, an inherent notion of uncertainty. When the point estimate leaves the confidence region, we trigger active learning experiments. We update models only after enforcing a sufficiently small uncertainty in the filter. Thus, models are only updated when necessary and statistically significant while ensuring guaranteed improvement, which we call event-triggered learning. We validate the proposed method in numerical simulations of a DC motor in combination with model predictive control.
On the Computational Consequences of Cost Function Design in Nonlinear Optimal Control
Authors: Tyler Westenbroek, Anand Siththaranjan, Mohsin Sarwari, Claire J. Tomlin, Shankar S. Sastry
Date: 2022-04-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Optimal control is an essential tool for stabilizing complex nonlinear systems. However, despite the extensive impacts of methods such as receding horizon control, dynamic programming and reinforcement learning, the design of cost functions for a particular system often remains a heuristic-driven process of trial and error. In this paper we seek to gain insights into how the choice of cost function interacts with the underlying structure of the control system and impacts the amount of computation required to obtain a stabilizing controller. We treat the cost design problem as a two-step process where the designer specifies outputs for the system that are to be penalized and then modulates the relative weighting of the inputs and the outputs in the cost. To characterize the computational burden associated to obtaining a stabilizing controller with a particular cost, we bound the prediction horizon required by receding horizon methods and the number of iterations required by dynamic programming methods to meet this requirement. Our theoretical results highlight a qualitative separation between what is possible, from a design perspective, when the chosen outputs induce either minimum-phase or non-minimum-phase behavior. Simulation studies indicate that this separation also holds for modern reinforcement learning methods.
Information-Theoretic Policy Learning from Partial Observations with Fully Informed Decision Makers
Authors: Tom Lefebvre
Date: 2022-04-04 00:00:00
Tasks: Behavioural cloning, Imitation Learning

In this work we formulate and treat an extension of the Imitation from Observations problem. Imitation from Observations is a generalisation of the well-known Imitation Learning problem where state-only demonstrations are considered. In our treatment we extend the scope of Imitation from Observations to feature-only demonstrations which could arguably be described as partial observations. Therewith we mean that the full state of the decision makers is unknown and imitation must take place on the basis of a limited set of features. We set out for methods that extract an executable policy directly from those features which, in the literature, would be referred to as Behavioural Cloning methods. Our treatment combines elements from probability and information theory and draws connections with entropy regularized Markov Decision Processes.
Finitely Heterogeneous Treatment Effect in Event-study
Authors: Myungkou Shin
Date: 2022-04-05 00:00:00
Tasks: Vocal Bursts Type Prediction

A key assumption of the differences-in-differences designs is that the average evolution of untreated potential outcomes is the same across different treatment cohorts: a parallel trends assumption. In this paper, we relax the parallel trend assumption by assuming a latent type variable and developing a type-specific parallel trend assumption. With a finite support assumption on the latent type variable and long pretreatment time periods, we show that an extremum classifier consistently estimates the type assignment. Based on the classification result, we propose a type-specific diff-in-diff estimator for type-specific ATT. By estimating the type-specific ATT, we study heterogeneity in treatment effect, in addition to heterogeneity in baseline outcomes.
Asymptotic Theory for Unit Root Moderate Deviations in Quantile Autoregressions and Predictive Regressions
Authors: Christis Katsouris
Date: 2022-04-05 00:00:00
Tasks: Time Series, Time Series Analysis

We establish the asymptotic theory in quantile autoregression when the model parameter is specified with respect to moderate deviations from the unit boundary of the form (1 + c / k) with a convergence sequence that diverges at a rate slower than the sample size n. Then, extending the framework proposed by Phillips and Magdalinos (2007), we consider the limit theory for the near-stationary and the near-explosive cases when the model is estimated with a conditional quantile specification function and model parameters are quantile-dependent. Additionally, a Bahadur-type representation and limiting distributions based on the M-estimators of the model parameters are derived. Specifically, we show that the serial correlation coefficient converges in distribution to a ratio of two independent random variables. Monte Carlo simulations illustrate the finite-sample performance of the estimation procedure under investigation.
Macroeconomic Dynamics in a finite world: the Thermodynamic Potential Approach
Authors: Éric Herbert, and Gael Giraud, Aurélie Louis-Napoléon, Christophe Goupil
Date: 2022-04-05 00:00:00
Tasks: Friction

This paper presents a conceptual model describing the medium and long-term co-evolution of natural and socio-economic subsystems of Earth. An economy is viewed as an out-of-equilibrium dissipative structure that can only be maintained with a flow of energy and matter. The distinctive approach emphasized here consists in capturing the economic impact of natural ecosystems being depleted and destroyed by human activities via a pinch of thermodynamic potentials. This viewpoint allows: (i) the full-blown integration of a limited quantity of primary resources into a non-linear macrodynamics that is stock-flow consistent both in terms of matter-energy as well as economic transactions; (ii) the inclusion of natural and forced recycling; (iii) the inclusion of a friction term which reflects the impossibility of producing goods and services in high metabolising intensity without exuding energy and matter wastes; (iv) the computation of the anthropically produced entropy as a function of intensity and friction. Analysis and numerical computations confirm the role played by intensity and friction as key factors for sustainability. Our approach is flexible enough to allow for various economic models to be embedded into our thermodynamic framework.
You are what your parents expect: Height and local reference points
Authors: Fan Wang, Esteban Puentes, Jere R. Behrman, Flávio Cunha
Date: 2022-04-05 00:00:00

Recent estimates are that about 150 million children under five years of age are stunted, with substantial negative consequences for their schooling, cognitive skills, health, and economic productivity. Therefore, understanding what determines such growth retardation is significant for designing public policies that aim to address this issue. We build a model for nutritional choices and health with reference-dependent preferences. Parents care about the health of their children relative to some reference population. In our empirical model, we use height as the health outcome that parents target. Reference height is an equilibrium object determined by earlier cohorts' parents' nutritional choices in the same village. We explore the exogenous variation in reference height produced by a protein-supplementation experiment in Guatemala to estimate our model's parameters. We use our model to decompose the impact of the protein intervention on height into price and reference-point effects. We find that the changes in reference points account for 65% of the height difference between two-year-old children in experimental and control villages in the sixth annual cohort born after the initiation of the intervention.
Causal Discovery of Macroeconomic State-Space Models
Authors: Emmet Hall-Hoffarth
Date: 2022-04-05 00:00:00
Tasks: Causal Discovery, State Space Models

This paper presents a set of tests and an algorithm for agnostic, data-driven selection among macroeconomic DSGE models inspired by structure learning methods for DAGs. As the log-linear state-space solution to any DSGE model is also a DAG it is possible to use associated concepts to identify a unique ground-truth state-space model which is compatible with an underlying DGP, based on the conditional independence relationships which are present in that DGP. In order to operationalise search for this ground-truth model, the algorithm tests feasible analogues of these conditional independence criteria against the set of combinatorially possible state-space models over observed variables. This process is consistent in large samples. In small samples the result may not be unique, so conditional independence tests can be combined with likelihood maximisation in order to select a single optimal model. The efficacy of this algorithm is demonstrated for simulated data, and results for real data are also provided and discussed.
Simple dominance of fixed priority top trading cycles
Authors: Pinaki Mandal
Date: 2022-04-05 00:00:00

We study the implementation of fixed priority top trading cycles (FPTTC) rules via simply dominant mechanisms (Pycia and Troyan, 2019) in the context of assignment problems, where agents are to be assigned at most one indivisible object and monetary transfers are not allowed. We consider both models - with and without outside options, and characterize all simply dominant FPTTC rules in both models. We further introduce the notion of simple strategy-proofness to resolve the issue with agents being concerned about having time-inconsistent preferences, and discuss its relation with simple dominance.
Low-Latency Speech Separation Guided Diarization for Telephone Conversations
Authors: Giovanni Morrone, Samuele Cornell, Desh Raj, Luca Serafini, Enrico Zovato, Alessio Brutti, Stefano Squartini
Date: 2022-04-05 00:00:00
Tasks: Action Detection, Activity Detection, speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition, Speech Separation

In this paper, we carry out an analysis on the use of speech separation guided diarization (SSGD) in telephone conversations. SSGD performs diarization by separating the speakers signals and then applying voice activity detection on each estimated speaker signal. In particular, we compare two low-latency speech separation models. Moreover, we show a post-processing algorithm that significantly reduces the false alarm errors of a SSGD pipeline. We perform our experiments on two datasets: Fisher Corpus Part 1 and CALLHOME, evaluating both separation and diarization metrics. Notably, our SSGD DPRNN-based online model achieves 11.1% DER on CALLHOME, comparable with most state-of-the-art end-to-end neural diarization models despite being trained on an order of magnitude less data and having considerably lower latency, i.e., 0.1 vs. 10 seconds. We also show that the separated signals can be readily fed to a speech recognition back-end with performance close to the oracle source signals.
A Comparison of Deep Learning MOS Predictors for Speech Synthesis Quality
Authors: Alessandro Ragano, Emmanouil Benetos, Michael Chinen, Helard B. Martinez, Chandan K. A. Reddy, Jan Skoglund, Andrew Hines
Date: 2022-04-05 00:00:00
Tasks: Benchmarking, Self-Supervised Learning, Speech Synthesis

Speech synthesis quality prediction has made remarkable progress with the development of supervised and self-supervised learning (SSL) MOS predictors but some aspects related to the data are still unclear and require further study. In this paper, we evaluate several MOS predictors based on wav2vec 2.0 and the NISQA speech quality prediction model to explore the role of the training data, the influence of the system type, and the role of cross-domain features in SSL models. Our evaluation is based on the VoiceMOS challenge dataset. Results show that SSL-based models show the highest correlation and lowest mean squared error compared to supervised models. The key point of this study is that benchmarking the statistical performance of MOS predictors alone is not sufficient to rank models since potential issues hidden in the data could bias the evaluated performances.
Complex Recurrent Variational Autoencoder with Application to Speech Enhancement
Authors: Yuying Xie, Thomas Arildsen, Zheng-Hua Tan
Date: 2022-04-05 00:00:00
Tasks: Speech Enhancement

As an extension of variational autoencoder (VAE), complex VAE uses complex Gaussian distributions to model latent variables and data. This work proposes a complex recurrent VAE framework, specifically in which complex-valued recurrent neural network and L1 reconstruction loss are used. Firstly, to account for the temporal property of speech signals, this work introduces complex-valued recurrent neural network in the complex VAE framework. Besides, L1 loss is used as the reconstruction loss in this framework. To exemplify the use of the complex generative model in speech processing, we choose speech enhancement as the specific application in this paper. Experiments are based on the TIMIT dataset. The results show that the proposed method offers improvements on objective metrics in speech intelligibility and signal quality.
Disentangled Speech Representation Learning Based on Factorized Hierarchical Variational Autoencoder with Self-Supervised Objective
Authors: Yuying Xie, Thomas Arildsen, Zheng-Hua Tan
Date: 2022-04-05 00:00:00
Tasks: Disentanglement, Representation Learning, Speaker Recognition, speech-recognition, Speech Recognition, Speech Representation Learning, Voice Conversion

Disentangled representation learning aims to extract explanatory features or factors and retain salient information. Factorized hierarchical variational autoencoder (FHVAE) presents a way to disentangle a speech signal into sequential-level and segmental-level features, which represent speaker identity and speech content information, respectively. As a self-supervised objective, autoregressive predictive coding (APC), on the other hand, has been used in extracting meaningful and transferable speech features for multiple downstream tasks. Inspired by the success of these two representation learning methods, this paper proposes to integrate the APC objective into the FHVAE framework aiming at benefiting from the additional self-supervision target. The main proposed method requires neither more training data nor more computational cost at test time, but obtains improved meaningful representations while maintaining disentanglement. The experiments were conducted on the TIMIT dataset. Results demonstrate that FHVAE equipped with the additional self-supervised objective is able to learn features providing superior performance for tasks including speech recognition and speaker recognition. Furthermore, voice conversion, as one application of disentangled representation learning, has been applied and evaluated. The results show performance similar to baseline of the new framework on voice conversion.
Exploring the influence of fine-tuning data on wav2vec 2.0 model for blind speech quality prediction
Authors: Helard Becerra, Alessandro Ragano, Andrew Hines
Date: 2022-04-05 00:00:00
Tasks: Diversity

Recent studies have shown how self-supervised models can produce accurate speech quality predictions. Speech representations generated by the pre-trained wav2vec 2.0 model allows constructing robust predicting models using small amounts of annotated data. This opens the possibility of developing strong models in scenarios where labelled data is scarce. It is known that fine-tuning improves the model's performance; however, it is unclear how the data (e.g., language, amount of samples) used for fine-tuning is influencing that performance. In this paper, we explore how using different speech corpus to fine-tune the wav2vec 2.0 can influence its performance. We took four speech datasets containing degradations found in common conferencing applications and fine-tuned wav2vec 2.0 targeting different languages and data size scenarios. The fine-tuned models were tested across all four conferencing datasets plus an additional dataset containing synthetic speech and they were compared against three external baseline models. Results showed that fine-tuned models were able to compete with baseline models. Larger fine-tune data guarantee better performance; meanwhile, diversity in language helped the models deal with specific languages. Further research is needed to evaluate other wav2vec 2.0 models pre-trained with multi-lingual datasets and to develop prediction models that are more resilient to language diversity.
Unsupervised Data Selection via Discrete Speech Representation for ASR
Authors: Zhiyun Lu, Yongqiang Wang, Yu Zhang, Wei Han, Zhehuai Chen, Parisa Haghani
Date: 2022-04-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Self-Supervised Learning, speech-recognition, Speech Recognition

Self-supervised learning of speech representations has achieved impressive results in improving automatic speech recognition (ASR). In this paper, we show that data selection is important for self-supervised learning. We propose a simple and effective unsupervised data selection method which selects acoustically similar speech to a target domain. It takes the discrete speech representation available in common self-supervised learning frameworks as input, and applies a contrastive data selection method on the discrete tokens. Through extensive empirical studies we show that our proposed method reduces the amount of required pre-training data and improves the downstream ASR performance. Pre-training on a selected subset of 6% of the general data pool results in 11.8% relative improvements in LibriSpeech test-other compared to pre-training on the full set. On Multilingual LibriSpeech French, German, and Spanish test sets, selecting 6% data for pre-training reduces word error rate by more than 15% relatively compared to the full set, and achieves competitive results compared to current state-of-the-art performances.
Inverse Problems Are Solvable on Real Number Signal Processing Hardware
Authors: Holger Boche, Adalbert Fono, Gitta Kutyniok
Date: 2022-04-05 00:00:00

Despite the success of Deep Learning (DL) serious reliability issues such as non-robustness persist. An interesting aspect is, whether these problems arise due to insufficient tools or due to fundamental limitations of DL. We study this question from the computability perspective, i.e., we characterize the limits imposed by the applied hardware. For this, we focus on the class of inverse problems, which, in particular, encompasses any task to reconstruct data from measurements. On digital hardware, a conceptual barrier on the capabilities of DL for solving finite-dimensional inverse problems has in fact already been derived. This paper investigates the general computation framework of Blum-Shub-Smale (BSS) machines which allows the processing and storage of arbitrary real values. Although a corresponding real world computing device does not exist at the moment, research and development towards real number computing hardware, usually referred to by the term "neuromorphic computing", has increased in recent years. In this work, we show that the framework of BSS machines does enable the algorithmic solvability of finite dimensional inverse problems. Our results emphasize the influence of the considered computing model in questions of accuracy and reliability.