pwc-archive/papers-with-abstracts huggingface dataset:
PortaSpeech: Portable and High-Quality Generative Text-to-Speech
Authors: Yi Ren, Jinglin Liu, Zhou Zhao
Date: 2021-09-30 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: text-to-speech, Text to Speech, Text-To-Speech Synthesis, Vocal Bursts Intensity Prediction, Word Alignment

Non-autoregressive text-to-speech (NAR-TTS) models such as FastSpeech 2 and Glow-TTS can synthesize high-quality speech from the given text in parallel. After analyzing two kinds of generative NAR-TTS models (VAE and normalizing flow), we find that: VAE is good at capturing the long-range semantics features (e.g., prosody) even with small model size but suffers from blurry and unnatural results; and normalizing flow is good at reconstructing the frequency bin-wise details but performs poorly when the number of model parameters is limited. Inspired by these observations, to generate diverse speech with natural details and rich prosody using a lightweight architecture, we propose PortaSpeech, a portable and high-quality generative text-to-speech model. Specifically, 1) to model both the prosody and mel-spectrogram details accurately, we adopt a lightweight VAE with an enhanced prior followed by a flow-based post-net with strong conditional inputs as the main architecture. 2) To further compress the model size and memory footprint, we introduce the grouped parameter sharing mechanism to the affine coupling layers in the post-net. 3) To improve the expressiveness of synthesized speech and reduce the dependency on accurate fine-grained alignment between text and speech, we propose a linguistic encoder with mixture alignment combining hard inter-word alignment and soft intra-word alignment, which explicitly extracts word-level semantic information. Experimental results show that PortaSpeech outperforms other TTS models in both voice quality and prosody modeling in terms of subjective and objective evaluation metrics, and shows only a slight performance degradation when reducing the model parameters to 6.7M (about 4x model size and 3x runtime memory compression ratio compared with FastSpeech 2). Our extensive ablation studies demonstrate that each design in PortaSpeech is effective.
Federated Learning in ASR: Not as Easy as You Think
Authors: Wentao Yu, Jan Freiwald, Sören Tewes, Fabien Huennemeyer, Dorothea Kolossa
Date: 2021-09-30 00:00:00
Tasks: Federated Learning, speech-recognition, Speech Recognition

With the growing availability of smart devices and cloud services, personal speech assistance systems are increasingly used on a daily basis. Most devices redirect the voice recordings to a central server, which uses them for upgrading the recognizer model. This leads to major privacy concerns, since private data could be misused by the server or third parties. Federated learning is a decentralized optimization strategy that has been proposed to address such concerns. Utilizing this approach, private data is used for on-device training. Afterwards, updated model parameters are sent to the server to improve the global model, which is redistributed to the clients. In this work, we implement federated learning for speech recognition in a hybrid and an end-to-end model. We discuss the outcomes of these systems, which both show great similarities and only small improvements, pointing to a need for a deeper understanding of federated learning for speech recognition.
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution
Authors: James King, Ramon Viñas Torné, Alexander Campbell, Pietro Liò
Date: 2021-09-30 00:00:00
Tasks: Audio Generation, Audio Super-Resolution, Image Super-Resolution, Super-Resolution

There have been several successful deep learning models that perform audio super-resolution. Many of these approaches involve using preprocessed feature extraction which requires a lot of domain-specific signal processing knowledge to implement. Convolutional Neural Networks (CNNs) improved upon this framework by automatically learning filters. An example of a convolutional approach is AudioUNet, which takes inspiration from novel methods of upsampling images. Our paper compares the pre-upsampling AudioUNet to a new generative model that upsamples the signal before using deep learning to transform it into a more believable signal. Based on the EDSR network for image super-resolution, the newly proposed model outperforms UNet with a 20% increase in log spectral distance and a mean opinion score of 4.06 compared to 3.82 for the two times upsampling case. AudioEDSR also has 87% fewer parameters than AudioUNet. How incorporating AudioUNet into a Wasserstein GAN (with gradient penalty) (WGAN-GP) structure can affect training is also explored. Finally the effects artifacting has on the current state of the art is analysed and solutions to this problem are proposed. The methods used in this paper have broad applications to telephony, audio recognition and audio generation tasks.
USEV: Universal Speaker Extraction with Visual Cue
Authors: Zexu Pan, Meng Ge, Haizhou Li
Date: 2021-09-30 00:00:00

A speaker extraction algorithm seeks to extract the target speaker's speech from a multi-talker speech mixture. The prior studies focus mostly on speaker extraction from a highly overlapped multi-talker speech mixture. However, the target-interference speaker overlapping ratios could vary over a wide range from 0% to 100% in natural speech communication, furthermore, the target speaker could be absent in the speech mixture, the speech mixtures in such universal multi-talker scenarios are described as general speech mixtures. The speaker extraction algorithm requires an auxiliary reference, such as a video recording or a pre-recorded speech, to form top-down auditory attention on the target speaker. We advocate that a visual cue, i.e., lip movement, is more informative than an audio cue, i.e., pre-recorded speech, to serve as the auxiliary reference for speaker extraction in disentangling the target speaker from a general speech mixture. In this paper, we propose a universal speaker extraction network with a visual cue, that works for all multi-talker scenarios. In addition, we propose a scenario-aware differentiated loss function for network training, to balance the network performance over different target-interference speaker pairing scenarios. The experimental results show that our proposed method outperforms various competitive baselines for general speech mixtures in terms of signal fidelity.
A Frequency-Domain Approach to Nonlinear Negative Imaginary Systems Analysis
Authors: Di Zhao, Chao Chen, Sei Zhen Khong
Date: 2021-09-30 00:00:00

In this study, we extend the theory of negative imaginary (NI) systems to a nonlinear framework using a frequency-domain approach. The extended notion is completely characterized via a finite-frequency integration over a "kernel function" on energy-bounded input and output signal pairs. The notion is closely related to and carefully contrasted with the well-studied extension of negative imaginariness -- the theory of counterclockwise dynamics. A condition for feedback stability of the proposed nonlinear NI systems is then developed based on the technique of integral quadratic constraints. Examples and simulations on feedback interconnections of typical nonlinear systems are provided to demonstrate the effectiveness.
Learning generalized Nash equilibria in monotone games: A hybrid adaptive extremum seeking control approach
Authors: Suad Krilašević, Sergio Grammatico
Date: 2021-09-30 00:00:00

In this paper, we solve the problem of learning a generalized Nash equilibrium (GNE) in merely monotone games. First, we propose a novel continuous semi-decentralized solution algorithm without projections that uses first-order information to compute a GNE with a central coordinator. As the second main contribution, we design a gain adaptation scheme for the previous algorithm in order to alleviate the problem of improper scaling of the cost functions versus the constraints. Third, we propose a data-driven variant of the former algorithm, where each agent estimates their individual pseudogradient via zeroth-order information, namely, measurements of their individual cost function values. Finally, we apply our method to a perturbation amplitude optimization problem in oil extraction engineering.
A novel class of fixed-time consensus protocols for multi-agent systems with simple dynamics
Authors: Yuquan Chen, Fumian Wang, Bing Wang
Date: 2021-09-30 00:00:00

This paper investigates the fixed-time consensus problem for a class of multi-agent systems with simple dynamics. Unlike the traditional way to realize fixed-time convergence, a novel strategy using the property of periodic functions is proposed to achieve fixed-time convergence. On this basis, novel protocols for achieving fixed-time consensus and fixed-time average consensus are then given, where the upper bound of the consensus time is independent of initial conditions. Moreover, the result of fixed-time average consensus is extended to a more general case, where the weights of different states can be allocated in advance. Finally, the fixed-time consensus in the presence of disturbances is derived with the help of sliding mode control, where a fixed-time sliding manifold and fixed-time reaching law are designed. All the conclusions are demonstrated by dedicated simulation examples.
An optimal reeling control strategy for pumping airborne wind energy systems without wind speed feedback
Authors: Andrea Berra, Lorenzo Fagiano
Date: 2021-09-30 00:00:00

Pumping airborne wind energy (AWE) systems employ a kite to convert wind energy into electricity, through a cyclic reeling motion of the tether. The problem of computing the optimal reeling speed for the sake of maximizing the average cycle power is considered. The difficulty stems from two aspects: 1) the uncertain, time- (and space-) varying nature of wind speed, which can not be measured accurately, and 2) the need to consider, in the same optimization problem, the different operational phases of the power cycle. A new, model-based approach that solves this problem is proposed. In the design phase, a model of the AWE system is employed to collect data pertaining to the cycle power obtained with various reel-in/reel-out speed pairs, assuming known wind speed. Then, a nonlinear map, identified from these data, is used as cost function in an optimization program that computes the best reel-in and -out speed pairs for each wind speed. Finally, the optimization results are exploited to infer the link between optimal reeling speed and tether force, which are both measured with high accuracy. Such a link is used to design a feedback controller that computes the reeling speed based on the measured tether force, in order to converge on the optimal force-speed manifold. Simulation results with a realistic model illustrate the effectiveness of the approach.
Distributed Model Predictive Safety Certification for Learning-based Control
Authors: Simon Muntwiler, Kim P. Wabersich, Andrea Carron, Melanie N. Zeilinger
Date: 2019-11-05 00:00:00
Tasks: Model Predictive Control

While distributed algorithms provide advantages for the control of complex large-scale systems by requiring a lower local computational load and less local memory, it is a challenging task to design high-performance distributed control policies. Learning-based control algorithms offer promising opportunities to address this challenge, but generally cannot guarantee safety in terms of state and input constraint satisfaction. A recently proposed safety framework for centralized linear systems ensures safety by matching the learning-based input online with the initial input of a model predictive control law capable of driving the system to a terminal set known to be safe. We extend this idea to derive a distributed model predictive safety certification (DMPSC) scheme, which is able to ensure state and input constraint satisfaction when applying any learning-based control algorithm to an uncertain distributed linear system with dynamic couplings. The scheme is based on a distributed tube-based model predictive control (MPC) concept, where subsystems negotiate local tube sizes among neighbors in order to mitigate restrictiveness of the safety approach. In addition, we present a technique for generating a structured ellipsoidal robust positive invariant tube. In numerical simulations, we show that the safety framework ensures constraint satisfaction for an initially unsafe control policy and allows to improve overall control performance compared to robust distributed MPC.
Stability Constrained Reinforcement Learning for Real-Time Voltage Control
Authors: Yuanyuan Shi, Guannan Qu, Steven Low, Anima Anandkumar, Adam Wierman
Date: 2021-09-30 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world power systems has been hindered by a lack of formal stability and safety guarantees. In this paper, we propose a stability constrained reinforcement learning method for real-time voltage control in distribution grids and we prove that the proposed approach provides a formal voltage stability guarantee. The key idea underlying our approach is an explicitly constructed Lyapunov function that certifies stability. We demonstrate the effectiveness of the approach in case studies, where the proposed method can reduce the transient control cost by more than 30\% and shorten the response time by a third compared to a widely used linear policy, while always achieving voltage stability. In contrast, standard RL methods often fail to achieve voltage stability.
Time Coordination of Multiple UAVs over Switching Communication Networks with Digraph Topologies
Authors: Hyungsoo Kang, Hyung-Jin Yoon, Venanzio Cichella, Naira Hovakimyan, Petros Voulgaris
Date: 2021-09-30 00:00:00

This paper presents a time-coordination algorithm for multiple UAVs executing cooperative missions. Unlike previous algorithms, it does not rely on the assumption that the communication between UAVs is bidirectional. Thus, the topology of the inter-UAV information flow can be characterized by digraphs. To achieve coordination with weak connectivity, we design a switching law that orchestrates switching between jointly connected digraph topologies. In accordance with the law, the UAVs with a transmitter switch the topology of their coordination information flow. A Lyapunov analysis shows that a decentralized coordination controller steers coordination errors to a neighborhood of zero. Simulation results illustrate that the algorithm attains coordination objectives with significantly reduced inter-UAV communication compared to previous work.
A Convex Method of Generalized State Estimation using Circuit-theoretic Node-breaker Model
Authors: Shimiao Li, Amritanshu Pandey, Larry Pileggi
Date: 2021-09-29 00:00:00
Tasks: State Estimation

An accurate and up-to-date topology is critical for situational awareness of a power grid; however, wrong switch statuses due to physical damage, communication error, or cyber-attack, can often result in topology errors. To maintain situation awareness under the possible topology errors and bad data, this paper develops ckt-GSE, a circuit-theoretic generalized state estimation method using node-breaker (NB) model. Ckt- GSE is a convex and scalable model that jointly estimates AC state variables and network topology, with robustness against different data errors. The method first constructs an equivalent circuit representation of the AC power grid by developing and aggregating linear circuit models of SCADA meters, phasor measurement units(PMUs), and switching devices. Then based on this circuit, ckt-GSE defines a constrained optimization problem using weighted least absolute value (WLAV) objective to form a robust estimator. The problem is a Linear Programming (LP) problem whose solution includes accurate AC states and a sparse vector of noise terms to identify topology errors and bad data.This paper is the first to explore a circuit-theoretic approach for an AC-network constrained GSE algorithm that is: 1) applicable to the real-world data setting, 2) convex without relaxation, scalable with our circuit-based solver; and 3) robust with the ability to identify and reject different data errors
Joint Information and Mechanism Design for Queues with Heterogeneous Users
Authors: Nasimeh Heydaribeni, Achilleas Anastasopoulos
Date: 2021-09-29 00:00:00

We consider a queue with an unobservable backlog by the incoming users. There is an information designer that observes the queue backlog and makes recommendations to the users arriving at the queue whether to join or not to join the queue. The arriving users have payoff relevant private types. The users, upon arrival, send a message, that is supposed to be their type, to the information designer if they are willing to hear a recommendation. The information designer then creates a recommendation for that specific type of user. The users have to pay a tax in exchange for the information they receive. In this setting, the information designer has two types of commitments. The first commitment is the recommendation policy and the second commitment is the tax function. We combine mechanism design and information design to study a queuing system with heterogeneous users. In this setting, the information designer is a sender of the information in the information design aspect and a receiver in the mechanism design aspect of the model. We formulate an optimization problem that characterizes the solution of the joint design problem. We characterize the tax functions and provide structural results for the recommendation policy of the information designer.
Dynamical symmetry breaking through AI: The dimer self-trapping transition
Authors: G. P. Tsironis, G. D. Barmparis, D. K. Campbell
Date: 2021-09-20 00:00:00

The nonlinear dimer obtained through the nonlinear Schr{\"o}dinger equation has been a workhorse for the discovery the role nonlinearity plays in strongly interacting systems. While the analysis of the stationary states demonstrates the onset of a symmetry broken state for some degree of nonlinearity, the full dynamics maps the system into an effective $\phi^4$ model. In this latter context, the self-trapping transition is an initial condition dependent transfer of a classical particle over a barrier set by the nonlinear term. This transition has been investigated analytically and mathematically it is expressed through the hyperbolic limit of Jacobian elliptic functions. The aim of the present work is to recapture this transition through the use of methods of Artificial Intelligence (AI). Specifically, we used a physics motivated machine learning model that is shown to be able to capture the original dynamic self-trapping transition and its dependence on initial conditions. Exploitation of this result in the case of the non-degenerate nonlinear dimer gives additional information on the more general dynamics and helps delineate linear from nonlinear localization. This work shows how AI methods may be embedded in physics and provide useful tools for discovery.
Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion
Authors: Shamsulhaq Basir, Inanc Senocak
Date: 2021-09-30 00:00:00

Physics-informed neural networks (PINNs) have been proposed to learn the solution of partial differential equations (PDE). In PINNs, the residual form of the PDE of interest and its boundary conditions are lumped into a composite objective function as soft penalties. Here, we show that this specific way of formulating the objective function is the source of severe limitations in the PINN approach when applied to different kinds of PDEs. To address these limitations, we propose a versatile framework based on a constrained optimization problem formulation, where we use the augmented Lagrangian method (ALM) to constrain the solution of a PDE with its boundary conditions and any high-fidelity data that may be available. Our approach is adept at forward and inverse problems with multi-fidelity data fusion. We demonstrate the efficacy and versatility of our physics- and equality-constrained deep-learning framework by applying it to several forward and inverse problems involving multi-dimensional PDEs. Our framework achieves orders of magnitude improvements in accuracy levels in comparison with state-of-the-art physics-informed neural networks.
Nonparametric Bounds on Treatment Effects with Imperfect Instruments
Authors: Kyunghoon Ban, Désiré Kédagni
Date: 2021-09-30 00:00:00

This paper extends the identification results in Nevo and Rosen (2012) to nonparametric models. We derive nonparametric bounds on the average treatment effect when an imperfect instrument is available. As in Nevo and Rosen (2012), we assume that the correlation between the imperfect instrument and the unobserved latent variables has the same sign as the correlation between the endogenous variable and the latent variables. We show that the monotone treatment selection and monotone instrumental variable restrictions, introduced by Manski and Pepper (2000, 2009), jointly imply this assumption. Moreover, we show how the monotone treatment response assumption can help tighten the bounds. The identified set can be written in the form of intersection bounds, which is more conducive to inference. We illustrate our methodology using the National Longitudinal Survey of Young Men data to estimate returns to schooling.
Information Acquisition and Diffusion in Markets
Authors: Atabek Atayev, Maarten Janssen
Date: 2021-09-30 00:00:00

Consumers can acquire information through their own search efforts or through their social network. Information diffusion via word-of-mouth communication leads to some consumers free-riding on their "friends" and less information acquisition via active search. Free-riding also has an important positive effect, however, in that consumers that do not actively search themselves are more likely to be able to compare prices before purchase, imposing competitive pressure on firms. We show how market prices depend on the characteristics of the network and on search cost. For example, if the search cost becomes small, price dispersion disappears, while the price level converges to the monopoly level, implying that expected prices are decreasing for small enough search cost. More connected societies have lower market prices, while price dispersion remains even in fully connected societies.
Uncertain Product Availability in Search Markets
Authors: Atabek Atayev
Date: 2021-09-30 00:00:00

In many markets buyers are poorly informed about which firms sell the product (product availability) and prices, and therefore have to spend time to obtain this information. In contrast, sellers typically have a better idea about which rivals offer the product. Information asymmetry between buyers and sellers on product availability, rather than just prices, has not been scrutinized in the literature on consumer search. We propose a theoretical model that incorporates this kind of information asymmetry into a simultaneous search model. Our key finding is that greater product availability may harm buyers by mitigating their willingness to search and, thus, softening competition.
Nonlinear Prices, Homogeneous Goods, Search
Authors: Atabek Atayev
Date: 2021-09-30 00:00:00

We analyze competition on nonlinear prices in homogeneous goods markets with consumer search. In equilibrium firms offer two-part tariffs consisting of a linear price and lump-sum fee. The equilibrium production is socially efficient as the linear price of equilibrium two-part tariffs equals to the production marginal cost. Firms thus compete in lump-sum fees, which are dispersed in equilibrium. We show that sellers enjoy higher profit, whereas consumers are worse-off with two-part tariffs than with linear prices. The competition softens because with two-part tariffs firms can make effective per-consumer demand less elastic than the actual demand.