pwc-archive/papers-with-abstracts huggingface dataset:
SpeedyIBL: A Comprehensive, Precise, and Fast Implementation of Instance-Based Learning Theory
Authors: Thuy Ngoc Nguyen, Duy Nhat Phan, Cleotilde Gonzalez
Date: 2021-11-19 00:00:00
Tasks: Decision Making, Learning Theory

Instance-Based Learning Theory (IBLT) is a comprehensive account of how humans make decisions from experience during dynamic tasks. Since it was first proposed almost two decades ago, multiple computational models have been constructed based on IBLT (i.e., IBL models). These models have been demonstrated to be very successful in explaining and predicting human decisions in multiple decision making contexts. However, as IBLT has evolved, the initial description of the theory has become less precise, and it is unclear how its demonstration can be expanded to more complex, dynamic, and multi-agent environments. This paper presents an updated version of the current theoretical components of IBLT in a comprehensive and precise form. It also provides an advanced implementation of the full set of theoretical mechanisms, SpeedyIBL, to unlock the capabilities of IBLT to handle a diverse taxonomy of individual and multi-agent decision-making problems. SpeedyIBL addresses a practical computational issue in past implementations of IBL models, the curse of exponential growth, that emerges from memory-based tabular computations. When more observations accumulate over time, there is an exponential growth of the memory of instances that leads directly to an exponential slow down of the computational time. Thus, SpeedyIBL leverages parallel computation with vectorization to speed up the execution time of IBL models. We evaluate the robustness of SpeedyIBL over an existing implementation of IBLT in decision games of increased complexity. The results not only demonstrate the applicability of IBLT through a wide range of decision making tasks, but also highlight the improvement of SpeedyIBL over its prior implementation as the complexity of decision features and number of agents increase. The library is open sourced for the use of the broad research community.
Attention based end to end Speech Recognition for Voice Search in Hindi and English
Authors: Raviraj Joshi, Venkateshan Kannan
Date: 2021-11-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We describe here our work with automatic speech recognition (ASR) in the context of voice search functionality on the Flipkart e-Commerce platform. Starting with the deep learning architecture of Listen-Attend-Spell (LAS), we build upon and expand the model design and attention mechanisms to incorporate innovative approaches including multi-objective training, multi-pass training, and external rescoring using language models and phoneme based losses. We report a relative WER improvement of 15.7% on top of state-of-the-art LAS models using these modifications. Overall, we report an improvement of 36.9% over the phoneme-CTC system. The paper also provides an overview of different components that can be tuned in a LAS-based system.
Concurrent Transmission and Multiuser Detection of LoRa Signals
Authors: The Khai Nguyen, Ha H. Nguyen, Ebrahim Bedeer
Date: 2021-11-19 00:00:00

This paper investigates a new model to improve the scalability of low-power long-range (LoRa) networks by allowing multiple end devices (EDs) to simultaneously communicate with multiple multi-antenna gateways on the same frequency band and using the same spreading factor. The maximum likelihood (ML) decision rule is first derived for non-coherent detection of information bits transmitted by multiple devices. To overcome the high complexity of the ML detection, we propose a sub-optimal two-stage detection algorithm to balance the computational complexity and error performance. In the first stage, we identify transmit chirps (without knowing which EDs transmit them). In the second stage, we determine the EDs that transmit the specific chirps identified from the first stage. To improve the detection performance in the second stage, we also optimize the transmit powers of EDs to minimize the similarity, measured by the Jaccard coefficient, between the received powers of any pair of EDs. As the power control optimization problem is non-convex, we use concepts from successive convex approximation to transform it to an approximate convex optimization problem that can be solved iteratively and guaranteed to reach a sub-optimal solution. Simulation results demonstrate and justify the tradeoff between transmit power penalties and network scalability of the proposed LoRa network model. In particular, by allowing concurrent transmission of 2 or 3 EDs, the uplink capacity of the proposed network can be doubled or tripled over that of a conventional LoRa network, albeit at the expense of additional 3.0 or 4.7 dB transmit power.
A comparison of streaming models and data augmentation methods for robust speech recognition
Authors: Jiyeon Kim, Mehul Kumar, Dhananjaya Gowda, Abhinav Garg, Chanwoo Kim
Date: 2021-11-19 00:00:00
Tasks: Data Augmentation, Robust Speech Recognition, speech-recognition, Speech Recognition

In this paper, we present a comparative study on the robustness of two different online streaming speech recognition models: Monotonic Chunkwise Attention (MoChA) and Recurrent Neural Network-Transducer (RNN-T). We explore three recently proposed data augmentation techniques, namely, multi-conditioned training using an acoustic simulator, Vocal Tract Length Perturbation (VTLP) for speaker variability, and SpecAugment. Experimental results show that unidirectional models are in general more sensitive to noisy examples in the training set. It is observed that the final performance of the model depends on the proportion of training examples processed by data augmentation techniques. MoChA models generally perform better than RNN-T models. However, we observe that training of MoChA models seems to be more sensitive to various factors such as the characteristics of training sets and the incorporation of additional augmentations techniques. On the other hand, RNN-T models perform better than MoChA models in terms of latency, inference time, and the stability of training. Additionally, RNN-T models are generally more robust against noise and reverberation. All these advantages make RNN-T models a better choice for streaming on-device speech recognition compared to MoChA models.
Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions
Authors: Chunxi Liu, Michael Picheny, Leda Sari, Pooja Chitkara, Alex Xiao, Xiaohui Zhang, Mark Chou, Andres Alvarado, Caner Hazirbas, Yatharth Saraf
Date: 2021-11-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Fairness, speech-recognition, Speech Recognition

It is well known that many machine learning systems demonstrate bias towards specific groups of individuals. This problem has been studied extensively in the Facial Recognition area, but much less so in Automatic Speech Recognition (ASR). This paper presents initial Speech Recognition results on "Casual Conversations" -- a publicly released 846 hour corpus designed to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of metadata, including age, gender, and skin tone. The entire corpus has been manually transcribed, allowing for detailed ASR evaluations across these metadata. Multiple ASR models are evaluated, including models trained on LibriSpeech, 14,000 hour transcribed, and over 2 million hour untranscribed social media videos. Significant differences in word error rate across gender and skin tone are observed at times for all models. We are releasing human transcripts from the Casual Conversations dataset to encourage the community to develop a variety of techniques to reduce these statistical biases.
A Conformer-based ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement and Speech Separation
Authors: Tom O'Malley, Arun Narayanan, Quan Wang, Alex Park, James Walker, Nathan Howard
Date: 2021-11-18 00:00:00
Tasks: Acoustic echo cancellation, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition, Speech Separation

We present a frontend for improving robustness of automatic speech recognition (ASR), that jointly implements three modules within a single model: acoustic echo cancellation, speech enhancement, and speech separation. This is achieved by using a contextual enhancement neural network that can optionally make use of different types of side inputs: (1) a reference signal of the playback audio, which is necessary for echo cancellation; (2) a noise context, which is useful for speech enhancement; and (3) an embedding vector representing the voice characteristic of the target speaker of interest, which is not only critical in speech separation, but also helpful for echo cancellation and speech enhancement. We present detailed evaluations to show that the joint model performs almost as well as the task-specific models, and significantly reduces word error rate in noisy conditions even when using a large-scale state-of-the-art ASR model. Compared to the noisy baseline, the joint model reduces the word error rate in low signal-to-noise ratio conditions by at least 71% on our echo cancellation dataset, 10% on our noisy dataset, and 26% on our multi-speaker dataset. Compared to task-specific models, the joint model performs within 10% on our echo cancellation dataset, 2% on the noisy dataset, and 3% on the multi-speaker dataset.
Data-driven verification and synthesis of stochastic systems via barrier certificates
Authors: Ali Salamati, Abolfazl Lavaei, Sadegh Soudjani, Majid Zamani
Date: 2021-11-19 00:00:00

In this work, we study verification and synthesis problems for safety specifications over unknown discrete-time stochastic systems. When a model of the system is available, barrier certificates have been successfully applied for ensuring the satisfaction of safety specifications. In this work, we formulate the computation of barrier certificates as a robust convex program (RCP). Solving the acquired RCP is hard in general because the model of the system that appears in one of the constraints of the RCP is unknown. We propose a data-driven approach that replaces the uncountable number of constraints in the RCP with a finite number of constraints by taking finitely many random samples from the trajectories of the system. We thus replace the original RCP with a scenario convex program (SCP) and show how to relate their optimizers. We guarantee that the solution of the SCP is a solution of the RCP with a priori guaranteed confidence when the number of samples is larger than a pre-computed value. This provides a lower bound on the safety probability of the original unknown system together with a controller in the case of synthesis. We also discuss an extension of our verification approach to a case where the associated robust program is non-convex and show how a similar methodology can be applied. Finally, the applicability of our proposed approach is illustrated through three case studies.
A Risk-Managed Steady-State Analysis to Assess the Impact of Power Grid Uncertainties
Authors: Naeem Turner-Bandele, Amritanshu Pandey, Larry Pileggi
Date: 2021-11-19 00:00:00

Electricity systems are experiencing increased effects of randomness and variability due to emerging stochastic assets. The increased effects introduce new uncertainties into power systems that can impact system operability and reliability. Existing steady-state methods for assessing system-level operability and reliability are primarily deterministic, therefore, ill-suited to capture randomness and variability. This work introduces a probabilistic steady-state analysis inspired by statistical worst-case circuit analysis to evaluate the risk of operational violations due to stochastic resources. Compared to parallelized Monte Carlo analyses (MCS), we have seen up to 24x improvement in runtime speed using our approach without significant loss of probabilistic accuracy for a Texas7k low-wind day test system.
Learning-Based Repetitive Precision Motion Control with Mismatch Compensation
Authors: Efe C. Balta, Kira Barton, Dawn M. Tilbury, Alisa Rupenyan, John Lygeros
Date: 2021-11-19 00:00:00
Tasks: GPR

Learning-based control methods utilize run-time data from the underlying process to improve the controller performance under model mismatch and unmodeled disturbances. This is beneficial for optimizing industrial processes, where the dynamics are difficult to model, and the repetitive nature of the process can be exploited. In this work, we develop an iterative approach for repetitive precision motion control problems where the objective is to follow a reference geometry with minimal tracking error. Our method utilizes a nominal model of the process and learns the mismatch using Gaussian Process Regression (GPR). The control input and the GPR data are updated after each iteration to improve the performance in a run-to-run fashion. We provide a preliminary convergence analysis, implementation details of the proposed controller for minimizing different error types, and a case study where we demonstrate improved tracking performance with simulation and experimental results.
Benchmarks of Extended Basis Reachability Graphs
Authors: Yin Tong
Date: 2021-11-19 00:00:00

In this note, we want to provide a comparison among the efficiency of different approaches for the verification of K-step and infinite-step opacity based on three different graphs: the Extended Basis Reachability Graph (EBRG), the Basis Reachability Graph (BRG) when applicable, and the Reachability Graph (RG).
Backstepping-based Integral Sliding Mode Control with Time Delay Estimation for Autonomous Underwater Vehicles
Authors: Hossein Nejatbakhsh Esfahani, Behdad Aminian, Esten Ingar Grøtli, Sebastien Gros
Date: 2021-11-19 00:00:00

The aim of this paper is to propose a high performance control approach for trajectory tracking of Autonomous Underwater Vehicles (AUVs). However, the controller performance can be affected by the unknown perturbations including model uncertainties and external time-varying disturbances in an undersea environment. To address this problem, a Backstepping-based Integral Sliding Mode Control with Time Delay Estimation (BS-ISMC-TDE) is designed. To improve the performance of a conventional backstepping control algorithm, an Integral Sliding Mode Control (ISMC) approach is adopted in the backstepping design to attenuate the steady-state error. Moreover, an adaptive Time Delay Estimation (TDE) strategy is proposed to provide an estimation of perturbations by observing the inputs and the states of the AUV one step into the past without an exact knowledge of the dynamics and the upper bound of uncertainties. From the simulation results, it is shown that the proposed control approach using both adaptive and conventional TDE parts outperforms a Backstepping-based Integral Sliding Mode Control (BS-ISMC).
A Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections
Authors: Robert Hult, Mario Zanon, Sebastien Gros, Paolo Falcone
Date: 2021-11-19 00:00:00
Tasks: Distributed Optimization

In this paper, we consider the optimal coordination of automated vehicles at intersections under fixed crossing orders. We formulate the problem using direct optimal control and exploit the structure to construct a semi-distributed primal-dual interior-point algorithm to solve it by parallelizing most of the computations. Differently from standard distributed optimization algorithms, where the optimization problem is split, in our approach we split the linear algebra steps, such that the algorithm takes the same steps as a fully centralized one, while still performing computations in a distributed fashion. We analyze the communication requirements of the algorithm, and propose an approximation scheme which can significantly reduce the data exchange. We demonstrate the effectiveness of the algorithm in hard but realistic scenarios, which show that the approximation leads to reductions in communicated data of almost 99\% of the exact formulation, at the expense of less than 1\% suboptimality.
Novel Real-Time EMT-TS Modeling Architecture for Feeder Blackstart Simulations
Authors: Victor Paduani, Bei Xu, David Lubkeman, Ning Lu
Date: 2021-11-19 00:00:00
Tasks: Benchmarking

This paper presents the development and benchmarking of a novel real-time electromagnetic-transient and transient-stability (EMT-TS) modeling architecture for distribution feeder restoration studies. The work presents for the first time in the literature a real-time EMT-TS testbed in which the grid-forming unit is simulated in EMT domain, operating as the slack bus of the phasor domain while including unbalanced voltage conditions. To evaluate the performance and limitations of the proposed model, an equivalent EMT testbed is developed to serve as a benchmark. First, the co-simulation framework and the domain coupling methodology are introduced. Next, the steady-state operation of the EMT-TS and EMT models are matched. Then, tests are conducted to analyze the transient performance of the proposed EMT-TS model when compared to its EMT benchmark. Results reveal that when utilizing a battery energy storage system (BESS) as the grid-forming unit, the EMT-TS testbed can maintain high accuracy for typical load steps.
Boost Distribution System Restoration with Emergency Communication Vehicles Considering Cyber-Physical Interdependence
Authors: Zhigang Ye, Chen Chen, Ruihuan Liu, Kai Wu, Zhaohong Bie, Guannan Lou, Wei Gu, Yubo Yuan
Date: 2021-11-19 00:00:00

Enhancing restoration capabilities of distribution systems is one of the main strategies for resilient power systems to cope with extreme events. However, most of the existing studies assume the communication infrastructures are intact for distribution automation, which is unrealistic. Motivated by the applications of the emergency communication vehicles (ECVs) in quickly setting up wireless communication networks after disasters, in this paper, we propose an integrated distribution system restoration (DSR) framework and optimization models, which can coordinate the repair crews, the distribution system (physical sectors), and the emergency communication (cyber sectors) to pick up unserved power loads as quickly as possible. Case studies validated the effectiveness of the proposed models and proved the benefit of considering ECVs and cyber-physical interdependencies in DSR.
Forward-looking persistent excitation in model predictive control
Authors: Sven Brüggemann, Robert R. Bitmead
Date: 2020-04-03 00:00:00
Tasks: model, Model Predictive Control, parameter estimation

This work deals with the problem of simultaneous regulation and model parameter estimation in adaptive model predictive control. We propose an adaptive model predictive control and conditions which guarantee a persistently exciting closed loop sequence by only looking forward in time into the receding prediction horizon. Earlier works needed to look backwards and preserve prior regressor data. Instead, we present a procedure for the offline generation of a persistently exciting reference trajectory perturbing the equilibrium. With the new approach we demonstrate exponential convergence of nonlinear systems under the influence of the adaptive model predictive control combined with a recursive least squares identifier with forgetting factor despite bounded noise. The results are, at this stage, local in state and parameter-estimate space.
Residual fourier neural operator for thermochemical curing of composites
Authors: Gengxiang Chen, Yingguang Li, Xu Liu, Qinglu Meng, Jing Zhou, Xiaozhong Hao
Date: 2021-11-15 00:00:00

During the curing process of composites, the temperature history heavily determines the evolutions of the field of degree of cure as well as the residual stress, which will further influence the mechanical properties of composite, thus it is important to simulate the real temperature history to optimize the curing process of composites. Since thermochemical analysis using Finite Element (FE) simulations requires heavy computational loads and data-driven approaches suffer from the complexity of highdimensional mapping. This paper proposes a Residual Fourier Neural Operator (ResFNO) to establish the direct high-dimensional mapping from any given cure cycle to the corresponding temperature histories. By integrating domain knowledge into a time-resolution independent parameterized neural network, the mapping between cure cycles to temperature histories can be learned using limited number of labelled data. Besides, a novel Fourier residual mapping is designed based on mode decomposition to accelerate the training and boost the performance significantly. Several cases are carried out to evaluate the superior performance and generalizability of the proposed method comprehensively.
Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm
Authors: Fei Feng, Siqi Liang, Sung-Liang Chen
Date: 2021-11-19 00:00:00
Tasks: Denoising, Image Enhancement

Acoustic-resolution photoacoustic microscopy (AR-PAM) is a promising tool for microvascular imaging. In the focal region, resolution of AR-PAM is determined by the ultrasound transducer and ultimately limited by acoustic diffraction. In the out-of-focus region, resolution deteriorates with increasing distance from the focal plane, which restricts depth of focus (DOF). Besides, a trade-off exists between resolution and DOF. Previously, synthetic aperture focusing technique (SAFT) and/or deconvolution methods have been demonstrated to enhance AR-PAM images. However, they suffer from issues in low resolution, low signal-to-noise ratio (SNR), and/or poor image fidelity. Here, we propose a novel algorithm for AR-PAM to enhance image resolution, SNR, and fidelity. The algorithm consists of a Fourier accumulation SAFT (FA-SAFT) and a directional model-based (D-MB) deconvolution method. Inspired from Fourier denoising technique and directional SAFT, FA-SAFT mainly compensates for the defocusing effect. Besides, D-MB deconvolution enhances the resolution as well as preserves the image fidelity, especially for the objects with line patterns such as microvasculature. Full width at half maximum of 26-31 um over DOF of 1.8 mm and minimum resolvable distance of 46-49 um are experimentally achieved by imaging tungsten wire phantom. Moreover, imaging of leaf skeleton phantom and in vivo imaging of mouse blood vessels also prove that our algorithm is capable of providing high-resolution, high-SNR, and good-fidelity results for complex structures and for in vivo applications.
Whose Bias?
Authors: Vasudha Jain, Mark Whitmeyer
Date: 2021-11-19 00:00:00

Law enforcement acquires costly evidence with the aim of securing the conviction of a defendant, who is convicted if a decision-maker's belief exceeds a certain threshold. Either law enforcement or the decision-maker is biased and is initially overconfident that the defendant is guilty. Although an innocent defendant always prefers an unbiased decision-maker, he may prefer that law enforcement have some bias to none. Nevertheless, fixing the level of bias, an innocent defendant may prefer that the decision-maker, not law enforcement, is biased.