pwc-archive/papers-with-abstracts huggingface dataset:
Towards Universal Speech Discrete Tokens: A Case Study for ASR and TTS
Authors: Yifan Yang, Feiyu Shen, Chenpeng Du, Ziyang Ma, Kai Yu, Daniel Povey, Xie Chen
Date: 2023-09-14 00:00:00
Tasks: Self-Supervised Learning, speech-recognition, Speech Recognition, Speech Synthesis

Self-supervised learning (SSL) proficiency in speech-related tasks has driven research into utilizing discrete tokens for speech tasks like recognition and translation, which offer lower storage requirements and great potential to employ natural language processing techniques. However, these studies, mainly single-task focused, faced challenges like overfitting and performance degradation in speech recognition tasks, often at the cost of sacrificing performance in multi-task scenarios. This study presents a comprehensive comparison and optimization of discrete tokens generated by various leading SSL models in speech recognition and synthesis tasks. We aim to explore the universality of speech discrete tokens across multiple speech tasks. Experimental results demonstrate that discrete tokens achieve comparable results against systems trained on FBank features in speech recognition tasks and outperform mel-spectrogram features in speech synthesis in subjective and objective metrics. These findings suggest that universal discrete tokens have enormous potential in various speech-related tasks. Our work is open-source and publicly available at https://github.com/k2-fsa/icefall.
Training Audio Captioning Models without Audio
Authors: Soham Deshmukh, Benjamin Elizalde, Dimitra Emmanouilidou, Bhiksha Raj, Rita Singh, Huaming Wang
Date: 2023-09-14 00:00:00
Tasks: Audio captioning, Decoder

Automated Audio Captioning (AAC) is the task of generating natural language descriptions given an audio stream. A typical AAC system requires manually curated training data of audio segments and corresponding text caption annotations. The creation of these audio-caption pairs is costly, resulting in general data scarcity for the task. In this work, we address this major limitation and propose an approach to train AAC systems using only text. Our approach leverages the multimodal space of contrastively trained audio-text models, such as CLAP. During training, a decoder generates captions conditioned on the pretrained CLAP text encoder. During inference, the text encoder is replaced with the pretrained CLAP audio encoder. To bridge the modality gap between text and audio embeddings, we propose the use of noise injection or a learnable adapter, during training. We find that the proposed text-only framework performs competitively with state-of-the-art models trained with paired audio, showing that efficient text-to-audio transfer is possible. Finally, we showcase both stylized audio captioning and caption enrichment while training without audio or human-created text captions.
Enhancing Child Vocalization Classification with Phonetically-Tuned Embeddings for Assisting Autism Diagnosis
Authors: Jialu Li, Mark Hasegawa-Johnson, Karrie Karahalios
Date: 2023-09-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Phoneme Recognition, Self-Supervised Learning, speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

The assessment of children at risk of autism typically involves a clinician observing, taking notes, and rating children's behaviors. A machine learning model that can label adult and child audio may largely save labor in coding children's behaviors, helping clinicians capture critical events and better communicate with parents. In this study, we leverage Wav2Vec 2.0 (W2V2), pre-trained on 4300-hour of home audio of children under 5 years old, to build a unified system for tasks of clinician-child speaker diarization and vocalization classification (VC). To enhance children's VC, we build a W2V2 phoneme recognition system for children under 4 years old, and we incorporate its phonetically-tuned embeddings as auxiliary features or recognize pseudo phonetic transcripts as an auxiliary task. We test our method on two corpora (Rapid-ABC and BabbleCor) and obtain consistent improvements. Additionally, we outperform the state-of-the-art performance on the reproducible subset of BabbleCor. Code available at https://huggingface.co/lijialudew
Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology
Authors: Xiaying Wang, Lan Mei, Victor Kartsch, Andrea Cossettini, Luca Benini
Date: 2023-09-14 00:00:00
Tasks: Continual Learning, EEG, Transfer Learning

Brain-machine interfaces (BMIs) have emerged as a transformative force in assistive technologies, empowering individuals with motor impairments by enabling device control and facilitating functional recovery. However, the persistent challenge of inter-session variability poses a significant hurdle, requiring time-consuming calibration at every new use. Compounding this issue, the low comfort level of current devices further restricts their usage. To address these challenges, we propose a comprehensive solution that combines a tiny CNN-based Transfer Learning (TL) approach with a comfortable, wearable EEG headband. The novel wearable EEG device features soft dry electrodes placed on the headband and is capable of on-board processing. We acquire multiple sessions of motor-movement EEG data and achieve up to 96% inter-session accuracy using TL, greatly reducing the calibration time and improving usability. By executing the inference on the edge every 100ms, the system is estimated to achieve 30h of battery life. The comfortable BMI setup with tiny CNN and TL paves the way to future on-device continual learning, essential for tackling inter-session variability and improving usability.
Frequency-adaptive control of a three-phase single-stage grid-connected photovoltaic system under grid voltage sags
Authors: Alexis B. Rey-Boué, N. F. Guerrero-Rodríguez, Johannes Stöckl, Thomas I. Strasser
Date: 2023-09-14 00:00:00

The low-voltage ride-through service is carried out in this paper according to the voltage profile described by the IEC 61400-21 European normative when short-duration voltage sags happen, and some instantaneous reactive power is delivered to the grid in accordance with the Spanish grid code; the mandatory limitation of the amplitude of the three-phase inverter currents to its nominal value is carried out with a novel control strategy, in which a certain amount of instantaneous constant active power can also be delivered to the grid when small or moderate voltage sags happen. A Multiple second order generalized integrator frequency-locked loop synchronization algorithm is employed in order to estimate the system frequency without harmonic distortions, as well as to output the positive- and the negative- sequence of the {\alpha}\b{eta} quantities of the three-phase grid voltages when balanced and unbalanced voltage sags happen in a frequency-adaptive scheme. The current control is carried out in the stationary reference frame, which guarantees the cancellation of the harmonic distortions in the utility grid currents using a Harmonic compensation structure, and the implementation of a constant active power control in order to protect the DC link capacitor from thermal stresses avoiding the appearance of large harmonic distortions at twice the fundamental frequency in the DC link voltage. A case study of a three-phase single-stage grid-connected PV system with a maximum apparent power about 500 kVA is tested with several simulations using MATLAB/SIMULINK firstly, and secondly, with some experiments using the Controller hardware-in-the-loop (CHIL) simulation technique for several types of voltage sags in order to do the final validation of the control algorithms.
Guaranteed approximations of arbitrarily quantified reachability problems
Authors: Eric Goubault, Sylvie Putot
Date: 2023-09-14 00:00:00
Tasks: Motion Planning

We propose an approach to compute inner and outer-approximations of the sets of values satisfying constraints expressed as arbitrarily quantified formulas. Such formulas arise for instance when specifying important problems in control such as robustness, motion planning or controllers comparison. We propose an interval-based method which allows for tractable but tight approximations. We demonstrate its applicability through a series of examples and benchmarks using a prototype implementation.
Stability Studies for Photovoltaic Integration using Power Hardware-in-the-Loop Experiments
Authors: Christina N. Papadimitriou, Chrysanthos Charalambous, Andreas Armenakis, Zoran Miletic, Werner Tremmel, Anja Banjac, Thomas I. Strasser, Venizelos Efthymiou, George E. Georghiou
Date: 2023-09-14 00:00:00

The electrical power network is gradually migrating from a centralized generation approach to a decentralized generation with high shares of renewable energy sources (RES). However, power systems with low shares of synchronous generation and consequently low total system inertia, are vulnerable to power imbalances. Such systems can experience frequency stability problems, such as high frequency excursions and higher rates of change of frequency even under small disturbances. This phenomenon is intensified when the grid under investigation has low or no interconnections (islanded) and thus the challenge for stable operation becomes more significant for the operators. This work focuses on how the frequency stability is affected when a photovoltaic (PV) inverter is integrated into a real non-interconnected distribution grid in Cyprus. In order to capture the realistic interactions of this integration, stability experiments in a hardware-in-the-loop (HIL) environment are performed with the aim to provide insightful results for the grid operator.
Impact of Excitation and Weighting Errors on Performance of Compact OTA Testing Systems
Authors: Alejandro Antón Ruiz, Andrés Alayón Glazunov
Date: 2023-09-14 00:00:00

This paper investigates the impact of complex excitation errors of the chamber array antenna on the accuracy of the test zone of a random line-of-sight over-the-air testing setup. First, several combinations of compact chamber arrays of lengths L and short distances D between the test zone and the chamber array, which emulate a plane wave impinging at the test zone are obtained. The chamber array is linear and uniform with 100 antenna elements, and a linear taper was applied to some of the elements to emulate a plane wave impinging at the test zone with more compact setups. A subset of L and D was chosen, providing compact over-the-air test setups that fulfilled the defined figures of merit, which assess the similarity of the obtained field distribution to that of a plane wave. The tolerance of the chosen setups to complex excitation errors of the chamber array was then investigated, concluding that these errors must be considered when defining appropriate L and D combinations. Moreover, the performance of the matched filter and zero-forcing algorithms is evaluated for errors of the device under test array weighting coefficients. A random line-of-sight over-the-air testing setup with two arrays was simulated, where one of the arrays emulated the desired signal and the other emulated the interference, observing that the errors were more significant at higher signal-to-noise ratios. Additionally, the zero-forcing algorithm was more sensitive to errors than the matched filter, which was expected since the accuracy of the former for interference suppression is critical.
Common Information Approach for Static Team Problems with Polish Spaces and Existence of Optimal Policies
Authors: Naci Saldi
Date: 2023-09-14 00:00:00

In this paper, we demonstrate the existence of team-optimal strategies for static teams under observation-sharing information structures. Assuming that agents can access shared observations, we begin by converting the team problem into an equivalent centralized stochastic control problem through the introduction of a topology on policies. We subsequently apply conventional methods from stochastic control to prove the existence of team-optimal strategies. This study expands upon the widely recognized common information approach for team problems, originally designed for discrete scenarios, and adapts it to a more abstract continuous framework. The primary difficulty in this context is to establish the appropriate topology on policies.
Achieving 45% efficiency of CIGS/CdS Solar Cell by adding GaAs using optimization techniques
Authors: Satyam Bhatti, Habib Ullah Manzoor, Ahmed Zoha, Rami Ghannam
Date: 2023-09-14 00:00:00

This paper proposes an efficient three-layered p-GaAs/p-CIGS/n-CdS (PPN), a unique solar cell architecture. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance than the ones utilizing cadmium sulfide (CdS). On the contrary, CIGS-based devices are more efficient, considering their device performance, environmentally benign nature, and reduced cost. Therefore, our paper proposes a numerical analysis of the homojunction PPN-junction GaAs solar cell structure along with n-ZnO front contact that was simulated using the Solar Cells Capacitance Simulator (SCAPS-1D) software. Moreover, we investigated optimization techniques for evaluating the effect of the thickness and the carrier density on the performance of the PPN layer on solar cell architecture. Subsequently, the paper discusses the electronic characteristics of adding GaAs material on the top of the conventional (PN) junction, further leading to improved values of the parameters, such as the power conversion efficiency (PCE), open-circuit voltage (VOC), fill factor (FF) and short-circuit current density (JSC) of the solar cell. The most promising results of our study show that adding the GaAs layer using the optimised values of thickness as 5 ({\mu}m) and carrier density as 1*1020 (1/cm) will result in the maximum PCE, VOC, FF, and JSC of 45.7%, 1.16V, 89.52% and 43.88 (mA/m2), respectively, for the proposed solar cell architecture.
Optimal Control for Indoor Vertical Farms Based on Crop Growth
Authors: Annalena Daniels, Michael Fink, Marion Leibold, Dirk Wollherr, Senthold Asseng
Date: 2023-09-14 00:00:00

Vertical farming allows for year-round cultivation of a variety of crops, overcoming environmental limitations and ensuring food security. This closed and highly controlled system allows the plants to grow in optimal conditions, so that they reach maturity faster and yield more than on a conventional outdoor farm. However, one of the challenges of vertical farming is the high energy consumption. In this work, we optimize wheat growth using an optimal control approach with two objectives: first, we optimize inputs such as water, radiation, and temperature for each day of the growth cycle, and second, we optimize the duration of the plant's growth period to achieve the highest possible yield over a whole year. For this, we use a nonlinear, discrete-time hybrid model based on a simple universal crop model that we adapt to make the optimization more efficient. Using our approach, we find an optimal trade-off between used resources, net profit of the yield, and duration of a cropping period, thus increasing the annual yield of crops significantly while keeping input costs as low as possible. This work demonstrates the high potential of control theory in the discipline of vertical farming.
$k$-Contraction in a Generalized Lurie System
Authors: Ron Ofir, Jean-Jacques Slotine, Michael Margaliot
Date: 2023-09-14 00:00:00

We derive a sufficient condition for $k$-contraction in a generalized Lurie system~(GLS), that is, the feedback connection of a nonlinear dynamical system and a memoryless nonlinear function. For $k=1$, this reduces to a sufficient condition for standard contraction. For $k=2$, this condition implies that every bounded solution of the GLS converges to an equilibrium, which is not necessarily unique. We demonstrate the theoretical results by analyzing $k$-contraction in a biochemical control circuit with nonlinear dissipation terms.
A Fuzzy Cascaded Proportional-Derivative Controller for Under-actuated Flexible Joint Manipulators Using Bayesian Optimization
Authors: Changyi Lei, Quanmin Zhu
Date: 2023-09-14 00:00:00
Tasks: Bayesian Optimization

This paper proposes a novel fuzzy cascaded Proportional-Derivative (PD) controller for under-actuated single-link flexible joint manipulators. The original flexible joint system is considered as two coupled $2^{nd}$-order sub-systems. The proposed controller is composed of two cascaded PD controllers and two fuzzy logic regulators (FLRs). The first (virtual) PD controller is used to generate desired control input that stabilizes the first $2^{nd}$-order sub-system. Solving the equation by considering the coupling terms as design variables, the reference signal is generated for the second sub-system. Then through simple compensation design, together with the second PD controller, the cascaded PD controller is derived. In order to further improve the performance, two FLRs are implemented that adaptively tune the parameters of PD controllers. Under natural assumptions, the cascaded fuzzy PD controller is proved to possess locally asymptotic stability. All the offline tuning processes are completed data-efficiently by Bayesian Optimization. The results in simulation illustrate the stability and validity of our proposed method. Besides, the idea of cascaded PD controller presented here may be extended as a novel control method for other under-actuated systems, and the stability analysis renders a new perspective towards the stability proof of all other fuzzy-enhanced PID controllers.
Symbolic Abstractions with Guarantees: A Data-Driven Divide-and-Conquer Strategy
Authors: Abolfazl Lavaei
Date: 2023-09-14 00:00:00

This article is concerned with a data-driven divide-and-conquer strategy to construct symbolic abstractions for interconnected control networks with unknown mathematical models. We employ a notion of alternating bisimulation functions (ABF) to quantify the closeness between state trajectories of an interconnected network and its symbolic abstraction. Consequently, the constructed symbolic abstraction can be leveraged as a beneficial substitute for the formal verification and controller synthesis over the interconnected network. In our data-driven framework, we first establish a relation between each unknown subsystem and its data-driven symbolic abstraction, so-called alternating pseudo-bisimulation function (APBF), with a guaranteed probabilistic confidence. We then provide compositional conditions based on max-type small-gain techniques to construct an ABF for an unknown interconnected network using APBF of its individual subsystems, constructed from data. We demonstrate the efficacy of our data-driven approach over a room temperature network composing 100 rooms with unknown models. We construct a symbolic abstraction from data for each room as an appropriate substitute of original system and compositionally synthesize controllers regulating the temperature of each room within a safe zone with some guaranteed probabilistic confidence.
Learning Tube-Certified Control using Robust Contraction Metrics
Authors: Vivek Sharma, Pan Zhao, Naira Hovakimyan
Date: 2023-09-14 00:00:00

Control design for general nonlinear robotic systems with guaranteed stability and/or safety in the presence of model uncertainties is a challenging problem. Recent efforts attempt to learn a controller and a certificate (e.g., a Lyapunov function or a contraction metric) jointly using neural networks (NNs), in which model uncertainties are generally ignored during the learning process. In this paper, for nonlinear systems subject to bounded disturbances, we present a framework for jointly learning a robust nonlinear controller and a contraction metric using a novel disturbance rejection objective that certifies a tube bound using NNs for user-specified variables (e.g. control inputs). The learned controller aims to minimize the effect of disturbances on the actual trajectories of state and/or input variables from their nominal counterparts while providing certificate tubes around nominal trajectories that are guaranteed to contain actual trajectories in the presence of disturbances. Experimental results demonstrate that our framework can generate tighter (smaller) tubes and a controller that is computationally efficient to implement.
Grid-Aware On-Route Fast-Charging Infrastructure Planning for Battery Electric Bus with Equity Considerations: A Case Study in South King County
Authors: Xinyi Zhao, Chaoyue Zhao, Grace Jia
Date: 2023-09-14 00:00:00
Tasks: Fairness

The transition from traditional bus fleets to zero-emission ones necessitates the development of effective planning models for battery electric bus (BEB) charging infrastructure. On-route fast charging stations, distinct from on-base charging stations, present unique challenges related to safe operation and power supply capacity, making it difficult to control grid operational costs. This paper establishes a novel framework that integrates the bus route network and power network, which leverages the inter-dependency between both networks to optimize the planning outcomes of on-route BEB charging stations in South King County. The problem is formulated as a mixed-integer second-order cone programming model, aiming to minimize the overall planning cost, which includes investments in charging equipment, power facility, and grid operation. Furthermore, fairness measurements are incorporated into the planning process, allowing for the consideration of both horizontal transit equity and vertical transit equity based on different zone merging criteria within the county's existing census tracts. The results of this planning model offer valuable insights into achieving both economic efficiency and social justice in the design of on-route charging facilities for BEBs in South King County.
Fast WDM provisioning with minimal probing: the first field experiments for DC exchanges
Authors: Hideki Nishizawa, Toru Mano, Thomas Ferreira de Lima, Yue-Kai Huang, Zehao Wang, Wataru Ishida, Masahisa Kawashima, Ezra Ip, Andrea D'Amico, Seiji Okamoto, Takeru Inoue, Kazuya Anazawa, Vittorio Curri, Gil Zussman, Daniel Kilper, Tingjun Chen, Ting Wang, Koji Asahi, Koichi Takasugi
Date: 2023-09-14 00:00:00

We propose an approach to estimate the end-to-end GSNR accurately in a short time when a data center interconnect (DCI) network operator receives a service request from users, not by measuring the GSNR at the operational route and wavelength for the End-End optical path but by simply applying a QoT probe channel link by link, at a convenient wavelength/modulation-format for measurement. Assuming connections between coherent transceivers of various frequency ranges, modulators, and modulation formats, we propose a new device software architecture in which the DCI network operator optimizes the transmission mode between user transceivers with high accuracy using only standard parameters such as Bit Error Rate. In this paper, we first experimentally built three different routes of 32 km/72 km/122 km in the C-band to confirm the accuracy of this approach. For the operational end-to-end GSNR measurements, the accuracy estimated from the sum of the measurements for each link was 0.6 dB, and the wavelength-dependent error was about 0.2 dB. Then, using field fibers deployed in the NSF COSMOS testbed (deployed in an urban area), a Linux-based transmission device software architecture, and coherent transceivers with different optical frequency ranges, modulators, and modulation formats, the fast WDM provisioning of an optical path was completed within 6 minutes (with a Q-factor error of about 0.7 dB).