pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing Energy-Efficient Braking Trajectories with Anticipatory Road Data for Automated Vehicles
Authors: Andres Alvarez Prado, Vladislav Nenchev, Christian Rathgeber
Date: 2024-06-25 00:00:00
Tasks: continuous-control, Continuous Control, Trajectory Planning

Trajectory planning in automated driving typically focuses on satisfying safety and comfort requirements within the vehicle's onboard sensor range. This paper introduces a method that leverages anticipatory road data, such as speed limits, road slopes, and traffic lights, beyond the local perception range to optimize energy-efficient braking trajectories. For that, coasting, which reduces energy consumption, and active braking are combined to transition from the current vehicle velocity to a lower target velocity at a given distance ahead. Finding the switching instants between the coasting phases and the continuous control for the braking phase is addressed as an optimal trade-off between maximizing coasting periods and minimizing braking effort. The resulting switched optimal control problem is solved by deriving necessary optimality conditions. To facilitate the incorporation of additional feasibility constraints for multi-phase trajectories, a sub-optimal alternative solution based on parametric optimization is proposed. Both methods are compared in simulation.
On Calibration of Speech Classification Models: Insights from Energy-Based Model Investigations
Authors: Yaqian Hao, Chenguang Hu, Yingying Gao, Shilei Zhang, Junlan Feng
Date: 2024-06-26 00:00:00
Tasks: Classification, Decision Making, Deep Learning

For speech classification tasks, deep learning models often achieve high accuracy but exhibit shortcomings in calibration, manifesting as classifiers exhibiting overconfidence. The significance of calibration lies in its critical role in guaranteeing the reliability of decision-making within deep learning systems. This study explores the effectiveness of Energy-Based Models in calibrating confidence for speech classification tasks by training a joint EBM integrating a discriminative and a generative model, thereby enhancing the classifiers calibration and mitigating overconfidence. Experimental evaluations conducted on three speech classification tasks specifically: age, emotion, and language recognition. Our findings highlight the competitive performance of EBMs in calibrating the speech classification models. This research emphasizes the potential of EBMs in speech classification tasks, demonstrating their ability to enhance calibration without sacrificing accuracy.
E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS
Authors: Sefik Emre Eskimez, Xiaofei Wang, Manthan Thakker, Canrun Li, Chung-Hsien Tsai, Zhen Xiao, Hemin Yang, Zirun Zhu, Min Tang, Xu Tan, Yanqing Liu, Sheng Zhao, Naoyuki Kanda
Date: 2024-06-26 00:00:00
Tasks: text-to-speech, Text to Speech

This paper introduces Embarrassingly Easy Text-to-Speech (E2 TTS), a fully non-autoregressive zero-shot text-to-speech system that offers human-level naturalness and state-of-the-art speaker similarity and intelligibility. In the E2 TTS framework, the text input is converted into a character sequence with filler tokens. The flow-matching-based mel spectrogram generator is then trained based on the audio infilling task. Unlike many previous works, it does not require additional components (e.g., duration model, grapheme-to-phoneme) or complex techniques (e.g., monotonic alignment search). Despite its simplicity, E2 TTS achieves state-of-the-art zero-shot TTS capabilities that are comparable to or surpass previous works, including Voicebox and NaturalSpeech 3. The simplicity of E2 TTS also allows for flexibility in the input representation. We propose several variants of E2 TTS to improve usability during inference. See https://aka.ms/e2tts/ for demo samples.
Speaker-Independent Acoustic-to-Articulatory Inversion through Multi-Channel Attention Discriminator
Authors: Woo-Jin Chung, Hong-Goo Kang
Date: 2024-06-25 00:00:00
Tasks: Self-Supervised Learning

We present a novel speaker-independent acoustic-to-articulatory inversion (AAI) model, overcoming the limitations observed in conventional AAI models that rely on acoustic features derived from restricted datasets. To address these challenges, we leverage representations from a pre-trained self-supervised learning (SSL) model to more effectively estimate the global, local, and kinematic pattern information in Electromagnetic Articulography (EMA) signals during the AAI process. We train our model using an adversarial approach and introduce an attention-based Multi-duration phoneme discriminator (MDPD) designed to fully capture the intricate relationship among multi-channel articulatory signals. Our method achieves a Pearson correlation coefficient of 0.847, marking state-of-the-art performance in speaker-independent AAI models. The implementation details and code can be found online.
A multi-speaker multi-lingual voice cloning system based on vits2 for limmits 2024 challenge
Authors: Xiaopeng Wang, Yi Lu, Xin Qi, Zhiyong Wang, Yuankun Xie, Shuchen Shi, Ruibo Fu
Date: 2024-06-22 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Voice Cloning

This paper presents the development of a speech synthesis system for the LIMMITS'24 Challenge, focusing primarily on Track 2. The objective of the challenge is to establish a multi-speaker, multi-lingual Indic Text-to-Speech system with voice cloning capabilities, covering seven Indian languages with both male and female speakers. The system was trained using challenge data and fine-tuned for few-shot voice cloning on target speakers. Evaluation included both mono-lingual and cross-lingual synthesis across all seven languages, with subjective tests assessing naturalness and speaker similarity. Our system uses the VITS2 architecture, augmented with a multi-lingual ID and a BERT model to enhance contextual language comprehension. In Track 1, where no additional data usage was permitted, our model achieved a Speaker Similarity score of 4.02. In Track 2, which allowed the use of extra data, it attained a Speaker Similarity score of 4.17.
Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Survey
Authors: Meng Cui, Xubo Liu, Haohe Liu, Jinzheng Zhao, Daoliang Li, Wenwu Wang
Date: 2024-06-20 00:00:00
Tasks: Multi-Task Learning

Digital aquaculture leverages advanced technologies and data-driven methods, providing substantial benefits over traditional aquaculture practices. This paper presents a comprehensive review of three interconnected digital aquaculture tasks, namely, fish tracking, counting, and behaviour analysis, using a novel and unified approach. Unlike previous reviews which focused on single modalities or individual tasks, we analyse vision-based (i.e. image- and video-based), acoustic-based, and biosensor-based methods across all three tasks. We examine their advantages, limitations, and applications, highlighting recent advancements and identifying critical cross-cutting research gaps. The review also includes emerging ideas such as applying multi-task learning and large language models to address various aspects of fish monitoring, an approach not previously explored in aquaculture literature. We identify the major obstacles hindering research progress in this field, including the scarcity of comprehensive fish datasets and the lack of unified evaluation standards. To overcome the current limitations, we explore the potential of using emerging technologies such as multimodal data fusion and deep learning to improve the accuracy, robustness, and efficiency of integrated fish monitoring systems. In addition, we provide a summary of existing datasets available for fish tracking, counting, and behaviour analysis. This holistic perspective offers a roadmap for future research, emphasizing the need for comprehensive datasets and evaluation standards to facilitate meaningful comparisons between technologies and to promote their practical implementations in real-world settings.
Unconditional Stability Analysis of N-Port Networks Based on Structured Singular Value Computation
Authors: Aimar Mateo, Ibone Lizarraga, Jorge Terrer, Aitziber Anakabe, J. M Collantes
Date: 2024-06-27 00:00:00
Tasks: Computational Efficiency

In this paper, a novel approach based on robust stability concepts and tools is introduced to evaluate the unconditional stability of microwave active $\textit{n}$-port devices. An efficient calculation of the Structured Singular Value of the $\textit{n}$x$\textit{n}$ scattering matrix is proposed to obtain the stability characteristics of the device. The presented method is validated in two ways. First, it is applied to a referential 4x4 scattering parameter set for independent verification. Second, the method is applied to a 4-port GaAs FET amplifier fabricated in hybrid technology. The results confirm the validity and computational efficiency of the proposed approach.
A Max Pressure Algorithm for Traffic Signals Considering Pedestrian Queues
Authors: Hao liu, Vikash V. Gayah, Michael Levin
Date: 2024-06-27 00:00:00

This paper proposes a novel max-pressure (MP) algorithm that incorporates pedestrian traffic into the MP control architecture. Pedestrians are modeled as being included in one of two groups: those walking on sidewalks and those queued at intersections waiting to cross. Traffic dynamics models for both groups are developed. Under the proposed control policy, the signal timings are determined based on the queue length of both vehicles and pedestrians waiting to cross the intersection. The proposed algorithm maintains the decentralized control structure, and the paper proves that it also exhibits the maximum stability property for both vehicles and pedestrians. Microscopic traffic simulation results demonstrate that the proposed model can improve the overall operational efficiency -- i.e., reduce person travel delays -- under various vehicle demand levels compared to the original queue-based MP (Q-MP) algorithm and a recently developed rule-based MP algorithm considering pedestrians. The Q-MP ignores the yielding behavior of right-turn vehicles to conflicting pedestrian movements, which leads to high delay for vehicles. On the other hand, the delay incurred by pedestrians is high from the rule-based model since it imposes large waiting time tolerance to guarantee the operational efficiency of vehicles. The proposed algorithm outperforms both models since the states of both vehicles and pedestrians are taken into consideration to determine signal timings.
OCC-MP: A Max-Pressure framework to prioritize transit and high occupancy vehicles
Authors: Tanveer Ahmed, Hao liu, Vikash V. Gayah
Date: 2024-06-27 00:00:00
Tasks: Traffic Signal Control

Max-pressure (MP) is a decentralized adaptive traffic signal control approach that has been shown to maximize throughput for private vehicles. However, MP-based signal control algorithms do not differentiate the movement of transit vehicles from private vehicles or between high and single-occupancy private vehicles. Prioritizing the movement of transit or other high occupancy vehicles (HOVs) is vital to reduce congestion and improve the reliability and efficiency of transit operations. This study proposes OCC-MP: a novel MP-based algorithm that considers both vehicle queues and passenger occupancies in computing the weights of movements. By weighing movements with higher passenger occupancies more heavily, transit and other HOVs are implicitly provided with priority, while accounting for any negative impacts of that priority on single occupancy vehicles. And, unlike rule-based transit signal priority (TSP) strategies, OCC-MP more naturally also accommodates conflicting transit routes at a signalized intersection and facilitates their movement, even in mixed traffic without dedicated lanes. Simulations on a grid network under varying demands and transit configurations demonstrate the effectiveness of OCC-MP at providing TSP while simultaneously reducing the negative impact imparted onto lower occupancy private vehicles. Furthermore, OCC-MP is shown to have a larger stable region for demand compared to rule-based TSP strategies integrated into the MP framework. The performance of OCC-MP is also shown to be robust to errors in passenger occupancy information from transit vehicles and can be applied when passenger occupancies of private vehicles are not available. Finally, OCC-MP can be applied in a partially connected vehicle (CV) environment when a subset of vehicles is able to provide information to the signal controller, outperforming baseline methods at low CV penetration rates.
In-situ Controller Autotuning by Bayesian Optimization for Closed-loop Feedback Control of Laser Powder Bed Fusion Process
Authors: Baris Kavas, Efe C. Balta, Michael R. Tucker, Raamadaas Krishnadas, Alisa Rupenyan, John Lygeros, Markus Bambach
Date: 2024-06-27 00:00:00
Tasks: Bayesian Optimization

Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the production of complex, high-criticality parts for various industries. This method relies on static parameter sets from extensive experimentation and simulations, hoping they remain stable and defect-free in production. Closed-loop control of LPBF can further enhance process stability and reduce defects despite complex thermal histories, process noise, hardware drift, and unexpected perturbations. Controller performance depends on parameter tuning, traditionally a manual, expertise-driven process with no guarantee of optimal performance and limited transferability between systems. This study proposes Bayesian Optimization (BO) to automate in-layer controller tuning by leveraging LPBF's layer-to-layer repetitive nature. Two approaches are introduced: online tuning, adjusting parameters iteratively during the process, and offline tuning, conducted in a setup such as laser exposures on a bare metal plate. These methods are experimentally implemented on an in-layer PI controller, and the performance is investigated on two wedge geometries prone to overheating. Results show that BO effectively tunes controllers using either method, significantly reducing overheating in controlled wedge specimens compared to uncontrolled ones. This study presents the first printed parts controlled by an in-layer controller subjected to microstructural analysis. Findings reveal partial presence of lack-of-fusion porosities due to insufficient laser power assigned by the controller, highlighting a significant challenge for utilizing laser power controllers. In summary, BO presents a promising method for automatic in-layer controller tuning in LPBF, enhancing control precision and mitigating overheating in production parts.
Parameter Dependent Chen--Fliess Series and Their Nonrecursive Interconnections
Authors: W. Steven Gray, Natalie Pham
Date: 2024-06-27 00:00:00

A class of parameter dependent Chen--Fliess series is introduced where the series coefficients are taken from a noncommutative ring of multivariable differential operators. Such series are shown in the linear case to represent formal solutions to Cauchy initial value problems for nonhomogeneous PDEs and thus are useful for characterizing the input-output maps of distributed control systems. It is also shown that this class of functional series is almost closed under the set of nonrecursive interconnections, that is, any finite combination of parallel and series interconnections without a closed-loop. Some sufficient conditions are needed for the series interconnection. Specific examples are given involving the transport equation and the wave equation.
Distributed Utility Optimization in Vehicular Communication Systems
Authors: Miguel A. Diaz-Ibarra, Daniel U. Campos-Delgado, Carlos A. Gutierrez, Jose M. Luna-Rivera, Francisco J. Cabrera-Almeida
Date: 2024-06-27 00:00:00
Tasks: Distributed Optimization

In this paper, we study the problem of utility maximization in the uplink of vehicle-to-infrastructure communication systems. The studied scenarios consider four practical aspects of mobile radio communication links: i) Interference between adjacent channels, ii) interference between roadside units along the way, iii) fast and slow channel fadings, and iv) Doppler shift effects. We present first the system model for the IEEE 802.11p standard, which considers a communication network between vehicles and roadside infrastructure. Next, we formulate the problem of utility maximization in the network, and propose a distributed optimization scheme. This distributed scheme is based on a two-loop feedback configuration, where an outer-loop establishes the optimal signal to interference-noise ratio (SINR) that maximizes the utility function per vehicle and defines a quality-of-service objective. Meanwhile, inner-control loops adjust the transmission power to achieve this optimal SINR reference in each vehicle node regardless of interference, time-varying channel profiles and network latency. The computation complexity of the distributed utility maximization scheme is analyzed for each feedback loop. Simulation results indicate that the proposed scheme reaches the objective SINRs that maximize utility and improve energy efficiency in the network with a low time cost. The results also show that the maximum utility is consistently achieved for different propagation scenarios inside the vehicular communication network.
Generalized Averaging Method for Power Electronics Modeling from DC to above Half the Switching Frequency
Authors: Hongchang Li, Kangping Wang, Jingyang Fang, Wenjie Chen, Xu Yang
Date: 2024-06-27 00:00:00

Modeling power electronic converters at frequencies close to or above half the switching frequency has been difficult due to the time-variant and discontinuous switching actions. This paper uses the properties of moving Fourier coefficients to develop the generalized averaging method, breaking though the limit of half the switching frequency. The paper also proposes the generalized average model for various switching signals, including pulse-width modulation (PWM), phase-shift modulation, pulse-frequency modulation (PFM), and state-dependent switching signals, so that circuits and modulators/controllers can be modeled separately and combined flexibly. Using the Laplace transform of moving Fourier coefficients, the coupling of signals and their sidebands at different frequencies is clearly described as the coupling of moving Fourier coefficients at the same frequency in a linear time-invariant system framework. The modeling method is applied to a PWM controlled boost converter, a V2 constant on-time controlled buck converter, and a PFM controlled LLC converter, for demonstration and validation. Experimental results of the converters in different operating modes show that the proposed models have higher accuracy than exiting models, especially in the frequency range close to or above half the switching frequency. The developed method can be applied to almost all types of power electronic converters.
Invited: Human-Inspired Distributed Wearable AI
Authors: Shreyas Sen, Arunashish Datta
Date: 2024-06-26 00:00:00

The explosive surge in Human-AI interactions, fused with a soaring fascination in wearable technology, has ignited a frenzy of innovation and the emergence of a myriad of Wearable AI devices, each wielding diverse form factors, tackling tasks from health surveillance to turbocharging productivity. This paper delves into the vision for wearable AI technology, addressing the technical bottlenecks that stand in the way of its promised advancements. Embracing a paradigm shift, we introduce a Human-Inspired Distributed Network for Wearable AI, enabled by high-speed ultra-low-power secure connectivity via the emerging 'Body as a Wire' (Wi-R) technology. This breakthrough acts as the missing link: the artificial nervous system, seamlessly interconnecting all wearables and implantables, ushering in a new era of interconnected intelligence, where featherweight, perpetually operating wearable AI nodes redefine the boundaries of possibility.
An open-source Autonomous Surface Vehicle for Acoustic Tracking, Bathymetric and Photogrammetric Surveys
Authors: Pierre Gogendeau, Sylvain Bonhommeau, Hassen Fourati, Mohan Julien, Matteo Contini, Thomas Chevrier, Anne Elise Nieblas, Serge Bernard
Date: 2024-06-26 00:00:00
Tasks: TAG

Autonomous Surface Vehicles (ASV) are becoming more affordable and include a wide variety of sensors and capacities with applications from ocean physics such as the Saildrone project to ecology with the tracking of marine species in the wild. Here, we present a multi-modal, affordable, open source, and reproducible ASV to track marine animal in shallow waters, collect information on bathymetry, and carry out photogrammetry surveys. The current specification enables scientists to track an animal equipped with an acoustic tag for 5~h and a spatial accuracy of 1~m. For bathymetric or photogrammetry surveys, the ASV can cover 100 x 100~m areas in 2~h with a distance of 1-m between transects. Depending on the sensors included in the ASV, it has a price ranging from \$2,434 to \$11,072. We illustrate these developments with a case study and a field survey for each of the different application proposed.
Battery Operations in Electricity Markets: Strategic Behavior and Distortions
Authors: Jerry Anunrojwong, Santiago R. Balseiro, Omar Besbes, Bolun Xu
Date: 2024-06-26 00:00:00

Electric power systems are undergoing a major transformation as they integrate intermittent renewable energy sources, and batteries to smooth out variations in renewable energy production. As privately-owned batteries grow from their role as marginal "price-takers" to significant players in the market, a natural question arises: How do batteries operate in electricity markets, and how does the strategic behavior of decentralized batteries distort decisions compared to centralized batteries? We propose an analytically tractable model that captures salient features of the highly complex electricity market. We derive in closed form the resulting battery behavior and generation cost in three operating regimes: (i) no battery, (ii) centralized battery, and (ii) decentralized profit-maximizing battery. We establish that a decentralized battery distorts its discharge decisions in three ways. First, there is quantity withholding, i.e., discharging less than centrally optimal. Second, there is a shift in participation from day-ahead to real-time, i.e., postponing some of its discharge from day-ahead to real-time. Third, there is reduction in real-time responsiveness, or discharging less in response to smoothing real-time demand than centrally optimal. We also quantify the impact of the battery market power on total system cost via the Price of Anarchy metric, and prove that the it is always between $9/8$ and $4/3$. That is, incentive misalignment always exists, but it is bounded even in the worst case. We calibrate our model to real data from Los Angeles and Houston. Lastly, we show that competition is very effective at reducing distortions, but many market power mitigation mechanisms backfire, and lead to higher total cost.