pwc-archive/papers-with-abstracts huggingface dataset:
TorchAudio: Building Blocks for Audio and Speech Processing
Authors: Yao-Yuan Yang, Moto Hira, Zhaoheng Ni, Anjali Chourdia, Artyom Astafurov, Caroline Chen, Ching-Feng Yeh, Christian Puhrsch, David Pollack, Dmitriy Genzel, Donny Greenberg, Edward Z. Yang, Jason Lian, Jay Mahadeokar, Jeff Hwang, Ji Chen, Peter Goldsborough, Prabhat Roy, Sean Narenthiran, Shinji Watanabe, Soumith Chintala, Vincent Quenneville-Bélair, Yangyang Shi
Date: 2021-10-28 00:00:00
Tasks: BIG-bench Machine Learning, GPU, valid

This document describes version 0.10 of TorchAudio: building blocks for machine learning applications in the audio and speech processing domain. The objective of TorchAudio is to accelerate the development and deployment of machine learning applications for researchers and engineers by providing off-the-shelf building blocks. The building blocks are designed to be GPU-compatible, automatically differentiable, and production-ready. TorchAudio can be easily installed from Python Package Index repository and the source code is publicly available under a BSD-2-Clause License (as of September 2021) at https://github.com/pytorch/audio. In this document, we provide an overview of the design principles, functionalities, and benchmarks of TorchAudio. We also benchmark our implementation of several audio and speech operations and models. We verify through the benchmarks that our implementations of various operations and models are valid and perform similarly to other publicly available implementations.
Continuous Speech Separation with Recurrent Selective Attention Network
Authors: Yixuan Zhang, Zhuo Chen, Jian Wu, Takuya Yoshioka, Peidong Wang, Zhong Meng, Jinyu Li
Date: 2021-10-28 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Separation

While permutation invariant training (PIT) based continuous speech separation (CSS) significantly improves the conversation transcription accuracy, it often suffers from speech leakages and failures in separation at "hot spot" regions because it has a fixed number of output channels. In this paper, we propose to apply recurrent selective attention network (RSAN) to CSS, which generates a variable number of output channels based on active speaker counting. In addition, we propose a novel block-wise dependency extension of RSAN by introducing dependencies between adjacent processing blocks in the CSS framework. It enables the network to utilize the separation results from the previous blocks to facilitate the current block processing. Experimental results on the LibriCSS dataset show that the RSAN-based CSS (RSAN-CSS) network consistently improves the speech recognition accuracy over PIT-based models. The proposed block-wise dependency modeling further boosts the performance of RSAN-CSS.
Data Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control
Authors: Cong Li, Fangzhou Liu, Yongchao Wang, Martin Buss
Date: 2021-10-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application towards continuous robotic tracking control, especially for high-dimensional robots. This work proposes a data-informed residual reinforcement learning (DR-RL) based robotic tracking control scheme applicable to robots with high dimensionality. The proposed DR-RL methodology outperforms common RL methods regarding sample efficiency and scalability. Specifically, we first decouple the original robot into low-dimensional robotic subsystems; and further utilize one-step backward (OSBK) data to construct incremental subsystems that are equivalent model-free representations of the above decoupled robotic subsystems. The formulated incremental subsystems allow for parallel learning to relieve computation load and offer us mathematical descriptions of robotic movements for conducting theoretical analysis. Then, we apply DR-RL to learn the tracking control policy, a combination of incremental base policy and incremental residual policy, under a parallel learning architecture. The incremental residual policy uses the guidance from the incremental base policy as the learning initialization and further learns from interactions with environments to endow the tracking control policy with adaptability towards dynamically changing environments. Our proposed DR-RL based tracking control scheme is developed with rigorous theoretical analysis of system stability and weight convergence. The effectiveness of our proposed method is validated numerically on a 7-DoF KUKA iiwa robot manipulator and experimentally on a 3-DoF robot manipulator that would fail for other counterpart RL methods.
Consensus-Based Decentralized Energy Trading for Distributed Energy Resources
Authors: Zhenyu Wang, XiaoYu Zhang, Hao Wang
Date: 2021-10-28 00:00:00
Tasks: energy management, energy trading, Management, Privacy Preserving

In smart grids, distributed energy resources (DERs) have penetrated residential zones to provide a new form of electricity supply, mainly from renewable energy. Residential households and commercial buildings with DERs have become prosumers in the local grids, since they can sell surplus power to others. Researches have been initiated to integrate and utilize DERs through better control and communication strategies. With the advances in the Internet of Things (IoT) technology, unprecedented coordination among DERs can be achieved to facilitate energy trading and transactive energy management. However, preventing leakage of users' information during the optimization process keeps challenging researchers, which drives them to develop privacy-preserving energy management systems. In this paper, we develop a fully decentralized transactive energy management using the consensus-based algorithm. To be specific, we design a virtual pool for prosumers to trade energy and exchange information with IoT technologies' support. The consensus-based algorithm enables prosumers to obtain the optimal energy schedule independently in a coordinated manner without revealing any personal data. We use real-world data to perform simulations and validate our developed algorithm. The results show that our consensus-based decentralized transactive energy management strategy is feasible and can significantly reduce the overall system cost.
Analyzing Photovoltaic's Impact on Conservation Voltage Reduction in Distribution Networks
Authors: Rui Cheng, Zhaoyu Wang, Yifei Guo, Fankun Bu
Date: 2021-10-27 00:00:00

Conservation voltage reduction (CVR) has been widely implemented in distribution networks and helped utilities effectively reduce energy and peak load. However, the increasing penetration level of solar photovoltaic (PV) has affected voltage profiles and the performance of CVR. It remains an outstanding question how CVR and solar PV interact with each other. Understanding this interaction is important for utilities in implementing CVR and assessing its performance. This paper studies the impact of solar PV on CVR in a real distribution system in the Midwest U.S. using comprehensive simulations. We have considered various PV allocations and penetration levels, as well as different inverter control modes according to IEEE Std 1547-2018. Three metrics are used to quantify the impact of solar PV on CVR: voltages at the substation, voltage distribution across the network, and energy consumption reduction due to CVR. The results show that the allocations of solar PV have the most significant effect on the CVR performance, where a dispersed allocation of solar PV will help flatten voltage profile and achieve deeper voltage reductions at the substation, less energy consumption and line losses.
Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking
Authors: Ahmed Aboudonia, Annika Eichler, Francesco Cordiano, Goran Banjac, John Lygeros
Date: 2020-07-01 00:00:00
Tasks: Distributed Optimization, Model Predictive Control

Various efforts have been devoted to developing stabilizing distributed Model Predictive Control (MPC) schemes for tracking piecewise constant references. In these schemes, terminal sets are usually computed offline and used in the MPC online phase to guarantee recursive feasibility and asymptotic stability. Maximal invariant terminal sets do not necessarily respect the distributed structure of the network, hindering the distributed implementation of the controller. On the other hand, ellipsoidal terminal sets respect the distributed structure, but may lead to conservative schemes. In this paper, a novel distributed MPC scheme is proposed for reference tracking of networked dynamical systems where the terminal ingredients are reconfigured online depending on the closed-loop states to alleviate the aforementioned issues. The resulting non-convex infinite-dimensional problem is approximated using a quadratic program. The proposed scheme is tested in simulation where the proposed MPC problem is solved using distributed optimization.
DDK: A Deep Koopman Approach for Dynamics Modeling and Trajectory Tracking of Autonomous Vehicles
Authors: Yongqian Xiao
Date: 2021-10-27 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Model Predictive Control, Motion Planning, Trajectory Prediction

Autonomous driving has attracted lots of attention in recent years. An accurate vehicle dynamics is important for autonomous driving techniques, e.g. trajectory prediction, motion planning, and control of trajectory tracking. Although previous works have made some results, the strong nonlinearity, precision, and interpretability of dynamics for autonomous vehicles are open problems worth being studied. In this paper, the approach based on the Koopman operator named deep direct Koopman (DDK) is proposed to identify the model of the autonomous vehicle and the identified model is a linear time-invariant (LTI) version, which is convenient for motion planning and controller design. In the approach, the Koopman eigenvalues and system matrix are considered as trainable tensors with the original states of the autonomous vehicle being concatenated to a part of the Koopman eigenfunctions so that a physically interpretable subsystem can be extracted from the identified latent dynamics. Subsequently, the process of the identification model is trained under the proposed method based on the dataset which consists of about 60km of data collected with a real electric SUV while the effectiveness of the identified model is validated. Meanwhile, a high-fidelity vehicle dynamics is identified in CarSim with DDK, and then, a linear model predictive control (MPC) called DDK-MPC integrating DDK is designed to validate the performance for the control of trajectory tracking. Simulation results illustrate that the model of the nonlinear vehicle dynamics can be identified effectively via the proposed method and that excellent tracking performance can be obtained with the identified model under DDK-MPC.
Accelerating Parameter Extraction of Power MOSFET Models Using Automatic Differentiation
Authors: Michihiro Shintani, Aoi Ueda, Takashi Sato
Date: 2021-10-22 00:00:00

The extraction of the model parameters is as important as the development of compact model itself because simulation accuracy is fully determined by the accuracy of the parameters used. This study proposes an efficient model-parameter extraction method for compact models of power MOSFETs. The proposed method employs automatic differentiation (AD), which is extensively used for training artificial neural networks. In the proposed AD-based parameter extraction, gradient of all the model parameters is analytically calculated by forming a graph that facilitates the backward propagation of errors. Based on the calculated gradient, computationally intensive numerical differentiation is eliminated and the model parameters are efficiently optimized. Experiments are conducted to fit current and capacitance characteristics of commercially available silicon carbide MOSFET using power MOSFET models having 13 model parameters. Results demonstrated that the proposed method could successfully derive the model parameters 3.50x faster than a conventional numerical-differentiation method while achieving the equal accuracy.
Solving it correctly Prevalence and Persistence of Gender Gap in Basic Mathematics in rural India
Authors: Upasak Das, Karan Singhal
Date: 2021-10-28 00:00:00

Mathematical ability is among the most important determinants of prospering in the labour market. Using multiple representative datasets with learning outcomes of over 2 million children from rural India in the age group 8 to 16 years, the paper examines the prevalence of gender gap in performance in mathematics and its temporal variation from 2010 to 2018. Our findings from the regressions show significant gender gap in mathematics, which is not observable for reading skills. This difference in mathematics scores remains prevalent across households of different socio-economic and demographic groups. This gap is found to be persistent over time and it appears to increase as the children get older. We also find significant inter-state variation with the north Indian states lagging behind considerably and the south Indian states showing a reverse gender gap. As an explanation to this, we observe evidence of a robust association between pre-existing gender norms at the household and district level with higher gender gap. The findings, in light of other available evidence on the consequences of such gaps, call for the need to understand these gender specific differences more granularly and periodically to inform gender-specific interventions.
Moral Hazard, Dynamic Incentives, and Ambiguous Perceptions
Authors: Martin Dumav
Date: 2021-10-28 00:00:00

This paper considers dynamic moral hazard settings, in which the consequences of the agent's actions are not precisely understood. In a new continuous-time moral hazard model with drift ambiguity, the agent's unobservable action translates to drift set that describe the evolution of output. The agent and the principal have imprecise information about the technology, and both seek robust performance from a contract in relation to their respective worst-case scenarios. We show that the optimal long-term contract aligns the parties' pessimistic expectations and broadly features compressing of the high-powered incentives. Methodologically, we provide a tractable way to formulate and characterize optimal long-run contracts with drift ambiguity. Substantively, our results provide some insights into the formal link between robustness and simplicity of dynamic contracts, in particular high-powered incentives become less effective in the presence of ambiguity.
Domestic Constraints in Crisis Bargaining
Authors: Liqun Liu
Date: 2021-10-28 00:00:00

I study how political bias and audience costs impose domestic institutional constraints that affect states' capacity to reach peaceful agreements during crises. With a mechanism design approach, I show that the existence of peaceful agreements hinges crucially on whether the resource being divided can appease two sides of the highest type (i.e. the maximum war capacity). The derivation has two major implications. On the one hand, if war must be averted, then political leaders are not incentivized by audience costs to communicate private information; they will pool on the strategy that induces the maximum bargaining gains. On the other hand, political bias matters for the scope of peace because it alters a state's expected war payoff.
Buy It Now or Later, or Not: Loss Aversion in Advance Purchasing
Authors: Senran Lin
Date: 2021-10-28 00:00:00

This paper studies the advance-purchase game when a consumer has belief-based loss-averse preferences, introducing a novel perspective by incorporating reference updating. It demonstrates that loss aversion increases the consumer's willingness to pre-purchase. Moreover, the paper endogenizes the seller's price-commitment behavior in the advance-purchase problem. The analysis reveals that the seller will commit to his spot price even with no obligation to do so, a behavior previously assumed in the literature.
Photon Limited Non-Blind Deblurring Using Algorithm Unrolling
Authors: Yash Sanghvi, Abhiram Gnanasambandam, Stanley H. Chan
Date: 2021-10-28 00:00:00
Tasks: Astronomy, Deblurring, Image Deblurring, Rolling Shutter Correction

Image deblurring in photon-limited conditions is ubiquitous in a variety of low-light applications such as photography, microscopy, and astronomy. However, the presence of photon shot noise due to low illumination and/or short exposure makes the deblurring task substantially more challenging than the conventional deblurring problems. In this paper, we present an algorithm unrolling approach for the photon-limited deblurring problem by unrolling a Plug-and-Play algorithm for a fixed number of iterations. By introducing a three-operator splitting formation of the Plug-and-Play framework, we obtain a series of differentiable steps which allows the fixed iteration unrolled network to be trained end-to-end. The proposed algorithm demonstrates significantly better image recovery compared to existing state-of-the-art deblurring approaches. We also present a new photon-limited deblurring dataset for evaluating the performance of algorithms.
Fast Video-based Face Recognition in Collaborative Learning Environments
Authors: Phuong Tran
Date: 2021-10-26 00:00:00
Tasks: Data Augmentation, Face Recognition

Face recognition is a classical problem in Computer Vision that has experienced significant progress. Yet, in digital videos, face recognition is complicated by occlusion, pose and lighting variations, and persons entering/leaving the scene. The thesis's goal is to develop a fast method for face recognition in digital videos that is applicable to large datasets. The thesis introduces several methods to address the problems associated with video face recognition. First, to address issues associated with pose and lighting variations, a collection of face prototypes is associated with each student. Second, to speed up the process, sampling, K-means Clustering, and a combination of both are used to reduce the number of face prototypes per student. Third, the videos are processed at different frame rates. Fourth, the thesis proposes the use of active sets to address occlusion and to eliminate face recognition application on video frames with slow face motions. Fifth, the thesis develops a group face detector that recognizes students within a collaborative learning group, while rejecting out-of-group face detections. Sixth, the thesis introduces a face DeID for protecting the students' identities. Seventh, the thesis uses data augmentation to increase the training set's size. The different methods are combined using multi-objective optimization to guarantee that the full method remains fast without sacrificing accuracy. To test the approach, the thesis develops the AOLME dataset of 138 student faces (81 boys and 57 girls) of ages 10 to 14, who are predominantly Latina/o students. Compared to the baseline method, the final optimized method resulted in fast recognition times with significant improvements in face recognition accuracy. Using face prototype sampling only, the proposed method achieved an accuracy of 71.8% compared to 62.3% for the baseline system, while running 11.6 times faster.
Residual Motion Compensation in Automotive MIMO SAR Imaging
Authors: Marco Manzoni, Marco Rizzi, Stefano Tebaldini, Andrea Virgilio Monti-Guarnieri, Claudio Maria Prati, Dario Tagliaferri, Monica Nicoli, Ivan Russo, Christian Mazzucco, Sergi Duque Biarge, Umberto Spagnolini
Date: 2021-10-28 00:00:00
Tasks: Motion Compensation

This paper deals with the analysis, estimation, and compensation of trajectory errors in automotive-based Synthetic Aperture Radar (SAR) systems. First of all, we define the geometry of the acquisition and the model of the received signal. We then proceed by analytically evaluating the effect of an error in the vehicle's trajectory. Based on the derived model, we introduce a motion compensation (MoCo) procedure capable of estimating and compensating constant velocity motion errors leading to a well-focused and well-localized SAR image. The procedure is validated using real data gathered by a 77 GHz automotive SAR with MIMO capabilities.
Multi-Beam Automotive SAR Imaging in Urban Scenarios
Authors: Marco Rizzi, Marco Manzoni, Stefano Tebaldini, Andrea Virgilio Monti-Guarnieri, Claudio Maria Prati, Dario Tagliaferri, Monica Nicoli, Ivan Russo, Christian Mazzucco, Simón Tejero Alfageme, Umberto Spagnolini
Date: 2021-10-28 00:00:00
Tasks: Autonomous Driving

Automotive synthetic aperture radar (SAR) systems are rapidly emerging as a candidate technological solution to enable a high-resolution environment mapping for autonomous driving. Compared to lidars and cameras, automotive-legacy radars can work in any weather condition and without an external source of illumination, but are limited in either range or angular resolution. SARs offer a relevant increase in angular resolution, provided that the ego-motion of the radar platform is known along the synthetic aperture. In this paper, we present the results of an experimental campaign aimed at assessing the potential of a multi-beam SAR imaging in an urban scenario, composed of various targets (buildings, cars, pedestrian, etc.), employing a 77 GHz multiple-input multiple-output (MIMO) radar platform based on a mass-market available automotive-grade technology. The results highlight a centimeter-level accuracy of the SAR images in realistic driving conditions, showing the possibility to use a multi-angle focusing approach to detect and discriminate between different targets based on their angular scattering response.
A Semi-Blind Method for Localization of Underwater Acoustic Sources
Authors: Amir Weiss, Toros Arikan, Hari Vishnu, Grant B. Deane, Andrew C. Singer, Gregory W. Wornell
Date: 2021-10-27 00:00:00

Underwater acoustic localization has traditionally been challenging due to the presence of unknown environmental structure and dynamic conditions. The problem is richer still when such structure includes occlusion, which causes the loss of line-of-sight (LOS) between the acoustic source and the receivers, on which many of the existing localization algorithms rely. We develop a semi-blind passive localization method capable of accurately estimating the source's position even in the possible absence of LOS between the source and all receivers. Based on typically-available prior knowledge of the water surface and bottom, we derive a closed-form expression for the optimal estimator under a multi-ray propagation model, which is suitable for shallow-water environments and high-frequency signals. By exploiting a computationally efficient form of this estimator, our methodology makes comparatively high-resolution localization feasible. We also derive the Cram\'er-Rao bound for this model, which can be used to guide the placement of collections of receivers so as to optimize localization accuracy. The method improves a balance of accuracy and robustness to environmental model mismatch, relative to existing localization methods that are useful in similar settings. The method is validated with simulations and water tank experiments.