pwc-archive/papers-with-abstracts huggingface dataset:
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization
Authors: Evelina Bakhturina, Yang Zhang, Boris Ginsburg
Date: 2022-03-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Text Normalization

Text normalization (TN) systems in production are largely rule-based using weighted finite-state transducers (WFST). However, WFST-based systems struggle with ambiguous input when the normalized form is context-dependent. On the other hand, neural text normalization systems can take context into account but they suffer from unrecoverable errors and require labeled normalization datasets, which are hard to collect. We propose a new hybrid approach that combines the benefits of rule-based and neural systems. First, a non-deterministic WFST outputs all normalization candidates, and then a neural language model picks the best one -- similar to shallow fusion for automatic speech recognition. While the WFST prevents unrecoverable errors, the language model resolves contextual ambiguity. The approach is easy to extend and we show it is effective. It achieves comparable or better results than existing state-of-the-art TN models.
Autoregressive Co-Training for Learning Discrete Speech Representations
Authors: Sung-Lin Yeh, Hao Tang
Date: 2022-03-29 00:00:00
Tasks: Quantization

While several self-supervised approaches for learning discrete speech representation have been proposed, it is unclear how these seemingly similar approaches relate to each other. In this paper, we consider a generative model with discrete latent variables that learns a discrete representation for speech. The objective of learning the generative model is formulated as information-theoretic co-training. Besides the wide generality, the objective can be optimized with several approaches, subsuming HuBERT-like training and vector quantization for learning discrete representation. Empirically, we find that the proposed approach learns discrete representation that is highly correlated with phonetic units, more correlated than HuBERT-like training and vector quantization.
Seq-2-Seq based Refinement of ASR Output for Spoken Name Capture
Authors: Karan Singla, Shahab Jalalvand, Yeon-Jun Kim, Ryan Price, Daniel Pressel, Srinivas Bangalore
Date: 2022-03-29 00:00:00
Tasks: Text Normalization

Person name capture from human speech is a difficult task in human-machine conversations. In this paper, we propose a novel approach to capture the person names from the caller utterances in response to the prompt "say and spell your first/last name". Inspired from work on spell correction, disfluency removal and text normalization, we propose a lightweight Seq-2-Seq system which generates a name spell from a varying user input. Our proposed method outperforms the strong baseline which is based on LM-driven rule-based approach.
Self-Supervised Learning for Recommender Systems: A Survey
Authors: Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, Zi Huang
Date: 2022-03-29 00:00:00
Tasks: Recommendation Systems, Self-Supervised Learning, Survey

In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data. Self-supervised learning (SSL), as an emerging technique for learning from unlabeled data, has attracted considerable attention as a potential solution to this issue. This survey paper presents a systematic and timely review of research efforts on self-supervised recommendation (SSR). Specifically, we propose an exclusive definition of SSR, on top of which we develop a comprehensive taxonomy to divide existing SSR methods into four categories: contrastive, generative, predictive, and hybrid. For each category, we elucidate its concept and formulation, the involved methods, as well as its pros and cons. Furthermore, to facilitate empirical comparison, we release an open-source library SELFRec (https://github.com/Coder-Yu/SELFRec), which incorporates a wide range of SSR models and benchmark datasets. Through rigorous experiments using this library, we derive and report some significant findings regarding the selection of self-supervised signals for enhancing recommendation. Finally, we shed light on the limitations in the current research and outline the future research directions.
To miss-attend is to misalign! Residual Self-Attentive Feature Alignment for Adapting Object Detectors
Authors: Vaishnavi Khindkar, Chetan Arora, Vineeth N Balasubramanian, Anbumani Subramanian, C. V. Jawahar
Date: 2022-01-05 00:00:00
Proceeding: WACV 2022 1
Tasks: Autonomous Navigation, Diversity, Object, object-detection, Object Detection, Unsupervised Domain Adaptation

Advancements in adaptive object detection can lead to tremendous improvements in applications like autonomous navigation, as they alleviate the distributional shifts along the detection pipeline. Prior works adopt adversarial learning to align image features at global and local levels, yet the instance-specific misalignment persists. Also, adaptive object detection remains challenging due to visual diversity in background scenes and intricate combinations of objects. Motivated by structural importance, we aim to attend prominent instance-specific regions, overcoming the feature misalignment issue. We propose a novel resIduaL seLf-attentive featUre alignMEnt (ILLUME) method for adaptive object detection. ILLUME comprises Self-Attention Feature Map (SAFM) module that enhances structural attention to object-related regions and thereby generates domain invariant features. Our approach significantly reduces the domain distance with the improved feature alignment of the instances. Qualitative results demonstrate the ability of ILLUME to attend important object instances required for alignment. Experimental results on several benchmark datasets show that our method outperforms the existing state-of-the-art approaches.
On handwriting pressure normalization for interoperability of different acquisition stylus
Authors: Marcos Faundez-Zanuy, Olga Brotons-Rufes, Carles Paul-Recarens, Réjean Plamondon
Date: 2022-03-28 00:00:00

In this paper, we present a pressure characterization and normalization procedure for online handwritten acquisition. Normalization process has been tested in biometric recognition experiments (identification and verification) using online signature database MCYT, which consists of the signatures from 330 users. The goal is to analyze the real mismatch scenarios where users are enrolled with one stylus and then, later on, they produce some testing samples using a different stylus model with different pressure response. Experimental results show: 1) a saturation behavior in pressure signal 2) different dynamic ranges in the different stylus studied 3) improved biometric recognition accuracy by means of pressure signal normalization as well as a performance degradation in mismatched conditions 4) interoperability between different stylus can be obtained by means of pressure normalization. Normalization produces an improvement in signature identification rates higher than 7% (absolute value) when compared with mismatched scenarios.
Packaging, containerization, and virtualization of computational omics methods: Advances, challenges, and opportunities
Authors: Mohammed Alser, Sharon Waymost, Ram Ayyala, Brendan Lawlor, Richard J. Abdill, Neha Rajkumar, Nathan LaPierre, Jaqueline Brito, Andre M. Ribeiro-dos-Santos, Can Firtina, Nour Almadhoun, Varuni Sarwal, Eleazar Eskin, Qiyang Hu, Derek Strong, Byoung-Do, Kim, Malak S. Abedalthagafi, Onur Mutlu, Serghei Mangul
Date: 2022-03-30 00:00:00

Omics software tools have reshaped the landscape of modern biology and become an essential component of biomedical research. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging, virtualization, and containerization are different approaches to satisfy this need by wrapping omics tools in additional software that makes the omics tools easier to install and use. Here, we systematically review practices across prominent packaging, virtualization, and containerization platforms. We outline the challenges, advantages, and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers, and system administrators. We also propose principles to make packaging, virtualization, and containerization of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research.
Enhanced Spreadsheet Computing with Finite-Domain Constraint Satisfaction
Authors: Ezana N. Beyenne, Hai-Feng Guo
Date: 2022-02-22 00:00:00

The spreadsheet application is among the most widely used computing tools in modern society. It provides excellent usability and usefulness, and it easily enables a non-programmer to perform programming-like tasks in a visual tabular "pen and paper" approach. However, spreadsheets are mostly limited to bookkeeping-like applications due to their mono-directional data flow. This paper shows how the spreadsheet computing paradigm is extended to break this limitation for solving constraint satisfaction problems. We present an enhanced spreadsheet system where finite-domain constraint solving is well supported in a visual environment. Furthermore, a spreadsheet-specific constraint language is constructed for general users to specify constraints among data cells in a declarative and scalable way. The new spreadsheet system significantly simplifies the development of many constraint-based applications using a visual tabular interface. Examples are given to illustrate the usability and usefulness of the extended spreadsheet paradigm. KEYWORDS: Spreadsheet computing, Finite-domain constraint satisfaction, Constraint logic programming
Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision Avoidance
Authors: James Guthrie, Marin Kobilarov, Enrique Mallada
Date: 2022-03-30 00:00:00
Tasks: Collision Avoidance, Form, Motion Planning

Motion planning methods for autonomous systems based on nonlinear programming offer great flexibility in incorporating various dynamics, objectives, and constraints. One limitation of such tools is the difficulty of efficiently representing obstacle avoidance conditions for non-trivial shapes. For example, it is possible to define collision avoidance constraints suitable for nonlinear programming solvers in the canonical setting of a circular robot navigating around M convex polytopes over N time steps. However, it requires introducing (2+L)MN additional constraints and LMN additional variables, with L being the number of halfplanes per polytope, leading to larger nonlinear programs with slower and less reliable solving time. In this paper, we overcome this issue by building closed-form representations of the collision avoidance conditions by outer-approximating the Minkowski sum conditions for collision. Our solution requires only MN constraints (and no additional variables), leading to a smaller nonlinear program. On motion planning problems for an autonomous car and quadcopter in cluttered environments, we achieve speedups of 4.8x and 8.7x respectively with significantly less variance in solve times and negligible impact on performance arising from the use of outer approximations.
Probing phoneme, language and speaker information in unsupervised speech representations
Authors: Maureen de Seyssel, Marvin Lavechin, Yossi Adi, Emmanuel Dupoux, Guillaume Wisniewski
Date: 2022-03-30 00:00:00
Tasks: Language Modelling

Unsupervised models of representations based on Contrastive Predictive Coding (CPC)[1] are primarily used in spoken language modelling in that they encode phonetic information. In this study, we ask what other types of information are present in CPC speech representations. We focus on three categories: phone class, gender and language, and compare monolingual and bilingual models. Using qualitative and quantitative tools, we find that both gender and phone class information are present in both types of models. Language information, however, is very salient in the bilingual model only, suggesting CPC models learn to discriminate languages when trained on multiple languages. Some language information can also be retrieved from monolingual models, but it is more diffused across all features. These patterns hold when analyses are carried on the discrete units from a downstream clustering model. However, although there is no effect of the number of target clusters on phone class and language information, more gender information is encoded with more clusters. Finally, we find that there is some cost to being exposed to two languages on a downstream phoneme discrimination task.
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings
Authors: Myunghun Jung, Hoirin Kim
Date: 2022-03-30 00:00:00
Tasks: Metric Learning, MULTI-VIEW LEARNING, Triplet, Word Embeddings

Acoustic word embeddings (AWEs) are discriminative representations of speech segments, and learned embedding space reflects the phonetic similarity between words. With multi-view learning, where text labels are considered as supplementary input, AWEs are jointly trained with acoustically grounded word embeddings (AGWEs). In this paper, we expand the multi-view approach into a proxy-based framework for deep metric learning by equating AGWEs with proxies. A simple modification in computing the similarity matrix allows the general pair weighting to formulate the data-to-proxy relationship. Under the systematized framework, we propose an asymmetric-proxy loss that combines different parts of loss functions asymmetrically while keeping their merits. It follows the assumptions that the optimal function for anchor-positive pairs may differ from one for anchor-negative pairs, and a proxy may have a different impact when it substitutes for different positions in the triplet. We present comparative experiments with various proxy-based losses including our asymmetric-proxy loss, and evaluate AWEs and AGWEs for word discrimination tasks on WSJ corpus. The results demonstrate the effectiveness of the proposed method.
Designing a Transactive Electric Vehicle Agent with Customer's Participation Preference
Authors: Ankit Singhal, Sarmad Hanif, Bishnu Bhattarai, Fernando B. dos Reis, Hayden Reeve, Robert Pratt
Date: 2022-03-30 00:00:00
Tasks: Privacy Preserving

The proliferation of electric vehicles (EVs) and their inherent flexibility in charging timings make them an asset to improve grid performance. In contrast to direct control by a utility or autonomous price-based charging, the transactive control framework not only provides benefits to both grid and customers but also ensures customer autonomy. In this work, we design a transactive electric vehicle (TEV) agent that incorporates the EV owner's willingness to trade-off between savings and amenity in form of a slider, where the EV owner's amenity is characterized as vehicle readiness. Further, a privacy-preserving bidding formulation is proposed that also represents the customer's transactive preference. A transactive market mechanism is discussed that integrates the TEV Agents into the local retail market and reconciles with the current day-ahead and real-time market structure. It is demonstrated that the proposed slider is able to provide a preferred trade-off between savings and amenity to individual customers. At the same time, the market mechanism is shown to successfully reduce both peak prices and peak demand. A comparative investigation of V1G and V2G technologies with respect to the battery prices is also discussed.
A Sequential Quadratic Programming Approach to the Solution of Open-Loop Generalized Nash Equilibria
Authors: Edward L. Zhu, Francesco Borrelli
Date: 2022-03-30 00:00:00
Tasks: Car Racing

Dynamic games can be an effective approach to modeling interactive behavior between multiple non-cooperative agents and they provide a theoretical framework for simultaneous prediction and control in such scenarios. In this work, we propose a numerical method for the solution of local generalized Nash equilibria (GNE) for the class of open-loop general-sum dynamic games for agents with nonlinear dynamics and constraints. In particular, we formulate a sequential quadratic programming (SQP) approach which requires only the solution of a single convex quadratic program at each iteration. Central to the robustness of our approach is a non-monotonic line search method and a novel merit function for SQP step acceptance. We show that our method achieves linear convergence in the neighborhood of local GNE and we derive an update rule for the merit function which helps to improve convergence from a larger set of initial conditions. We demonstrate the effectiveness of the algorithm in the context of car racing, where we show up to 32\% improvement of success rate when comparing against a state-of-the-art solution approach for dynamic games. \url{https://github.com/zhu-edward/DGSQP}.
Data-Driven Model Predictive Control for Linear Time-Periodic Systems
Authors: RuiQi Li, John W. Simpson-Porco, Stephen L. Smith
Date: 2022-03-30 00:00:00
Tasks: LEMMA, Model Predictive Control

We consider the problem of data-driven predictive control for an unknown discrete-time linear time-periodic (LTP) system of known period. Our proposed strategy generalizes both Data-enabled Predictive Control (DeePC) and Subspace Predictive Control (SPC), which are established data-driven control techniques for linear time-invariant (LTI) systems. The approach is supported by an extensive theoretical development of behavioral systems theory for LTP systems, culminating in a generalization of the fundamental lemma. Our algorithm produces results identical to standard Model Predictive Control (MPC) for deterministic LTP systems. Robustness of the algorithm to noisy data is illustrated via simulation of a regularized version of the algorithm applied to a stochastic multi-input multi-output LTP system.
A Barrier-Certified Optimal Coordination Framework for Connected and Automated Vehicles
Authors: Behdad Chalaki, Andreas A. Malikopoulos
Date: 2022-03-30 00:00:00
Tasks: Motion Planning

In this paper, we extend a framework that we developed earlier for coordination of connected and automated vehicles (CAVs) at a signal-free intersection by integrating a safety layer using control barrier functions. First, in our motion planning module, each CAV computes the optimal control trajectory using simple vehicle dynamics. The trajectory does not make any of the state, control, and safety constraints active. A vehicle-level tracking controller employs a combined feedforward-feedback control law to track the resulting optimal trajectory from the motion planning module. Then, a barrier-certificate module, acting as a middle layer between the vehicle-level tracking controller and physical vehicle, receives the control law from the vehicle-level tracking controller and using realistic vehicle dynamics ensures that none of the state, control, and safety constraints becomes active. The latter is achieved through a quadratic program, which can be solved efficiently in real time. We demonstrate the effectiveness of our extended framework through a numerical simulation.
Global Attitude Synchronization of Networked Rigid Bodies Under Directed Topologies
Authors: Fan Zhang, Deyuan Meng, Jingyao Zhang
Date: 2022-03-30 00:00:00

The global attitude synchronization problem is studied for networked rigid bodies under directed topologies. To avoid the asynchronous pitfall where only vector parts converge to some identical value but scalar parts do not, multiplicative quaternion errors are leveraged to develop attitude synchronization protocols for rigid bodies with the absolute measurements. It is shown that global synchronization of networked rigid bodies can be achieved if and only if the directed topology is quasi-strongly connected. Simultaneously, a novel double-energy-function analysis method, equipped with an ordering permutation technique about scalar parts and a coordinate transformation mechanism, is constructed for the quaternion behavior analysis of networked rigid bodies. In particular, global synchronization is achieved with our analysis method regardless of the highly nonlinear and strongly coupling problems resulting from multiplicative quaternion errors, which seriously hinder the traditional analysis of global synchronization for networked rigid bodies. Simulations for networked spacecraft are presented to show the global synchronization performances under different directed topologies.
Reliability of Open Public Electric Vehicle Direct Current Fast Chargers
Authors: David Rempel. Carleen Cullen, Mary Matteson Bryan, Gustavo Vianna Cezar
Date: 2022-03-30 00:00:00

In order to achieve a rapid transition to electric vehicle driving, a highly reliable and easy to use charging infrastructure is critical to building confidence as consumers shift from using familiar gas vehicles to unfamiliar electric vehicles (EV). This study evaluated the functionality of the charging system for 657 EVSE (electric vehicle service equipment) CCS connectors (combined charging system) on all 181 open, public DCFC (direct current fast chargers) charging stations in the Greater Bay Area. An EVSE was evaluated as functional if it charged an EV for 2 minutes or was charging an EV at the time the station was evaluated. Overall, 72.5% of the 657 EVSEs were functional. The cable was too short to reach the EV inlet for 4.9% of the EVSEs. Causes of 22.7% of EVSEs that were non-functioning were unresponsive or unavailable screens, payment system failures, charge initiation failures, network failures, or broken connectors. A random evaluation of 10% of the EVSEs, approximately 8 days after the first evaluation, demonstrated no overall change in functionality. This level of functionality appears to conflict with the 95 to 98% uptime reported by the EV service providers (EVSPs) who operate the EV charging stations. The findings suggest a need for shared, precise definitions of and calculations for reliability, uptime, downtime, and excluded time, as applied to open public DCFCs, with verification by third-party evaluation.
Rapid Scalable Distributed Power Flow with Open-Source Implementation
Authors: Xinliang Dai, Yichen Cai, Yuning Jiang, Veit Hagenmeyer
Date: 2022-03-30 00:00:00

This paper introduces a new method for solving the distributed AC power flow (PF) problem by further exploiting the problem formulation. We propose a new variant of the ALADIN algorithm devised specifically for this type of problem. This new variant is characterized by using a reduced modelling method of the distributed AC PF problem, which is reformulated as a zero-residual least-squares problem with consensus constraints. This PF is then solved by a Gauss-Newton based inexact ALADIN algorithm presented in the paper. An open-source implementation of this algorithm, called rapidPF+, is provided. Simulation results, for which the power system's dimension varies from 53 to 10224 buses, show great potential of this combination in the aspects of both the computing.