pwc-archive/papers-with-abstracts huggingface dataset:
Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval
Authors: Shunsuke Tsubaki, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada, Keisuke Imoto
Date: 2024-03-16 00:00:00
Tasks: Image Retrieval, Retrieval, Text Retrieval, Transfer Learning

The aim of this research is to refine knowledge transfer on audio-image temporal agreement for audio-text cross retrieval. To address the limited availability of paired non-speech audio-text data, learning methods for transferring the knowledge acquired from a large amount of paired audio-image data to shared audio-text representation have been investigated, suggesting the importance of how audio-image co-occurrence is learned. Conventional approaches in audio-image learning assign a single image randomly selected from the corresponding video stream to the entire audio clip, assuming their co-occurrence. However, this method may not accurately capture the temporal agreement between the target audio and image because a single image can only represent a snapshot of a scene, though the target audio changes from moment to moment. To address this problem, we propose two methods for audio and image matching that effectively capture the temporal information: (i) Nearest Match wherein an image is selected from multiple time frames based on similarity with audio, and (ii) Multiframe Match wherein audio and image pairs of multiple time frames are used. Experimental results show that method (i) improves the audio-text retrieval performance by selecting the nearest image that aligns with the audio information and transferring the learned knowledge. Conversely, method (ii) improves the performance of audio-image retrieval while not showing significant improvements in audio-text retrieval performance. These results indicate that refining audio-image temporal agreement may contribute to better knowledge transfer to audio-text retrieval.
Multi-Source Localization and Data Association for Time-Difference of Arrival Measurements
Authors: Gabrielle Flood, Filip Elvander
Date: 2024-03-15 00:00:00

In this work, we consider the problem of localizing multiple signal sources based on time-difference of arrival (TDOA) measurements. In the blind setting, in which the source signals are not known, the localization task is challenging due to the data association problem. That is, it is not known which of the TDOA measurements correspond to the same source. Herein, we propose to perform joint localization and data association by means of an optimal transport formulation. The method operates by finding optimal groupings of TDOA measurements and associating these with candidate source locations. To allow for computationally feasible localization in three-dimensional space, an efficient set of candidate locations is constructed using a minimal multilateration solver based on minimal sets of receiver pairs. In numerical simulations, we demonstrate that the proposed method is robust both to measurement noise and TDOA detection errors. Furthermore, it is shown that the data association provided by the proposed method allows for statistically efficient estimates of the source locations.
Audiosockets: A Python socket package for Real-Time Audio Processing
Authors: Nicolas Shu, David V. Anderson
Date: 2024-03-14 00:00:00

There are many packages in Python which allow one to perform real-time processing on audio data. Unfortunately, due to the synchronous nature of the language, there lacks a framework which allows for distributed parallel processing of the data without requiring a large programming overhead and in which the data acquisition is not blocked by subsequent processing operations. This work improves on packages used for audio data collection with a light-weight backend and a simple interface that allows for distributed processing through a socket-based structure. This is intended for real-time audio machine learning and data processing in Python with a quick deployment of multiple parallel operations on the same data, allowing users to spend less time debugging and more time developing.
On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems
Authors: Cristian Cioflan, Lukas Cavigelli, Manuele Rusci, Miguel de Prado, Luca Benini
Date: 2024-03-12 00:00:00
Tasks: Domain Adaptation, Keyword Spotting

Keyword spotting accuracy degrades when neural networks are exposed to noisy environments. On-site adaptation to previously unseen noise is crucial to recovering accuracy loss, and on-device learning is required to ensure that the adaptation process happens entirely on the edge device. In this work, we propose a fully on-device domain adaptation system achieving up to 14% accuracy gains over already-robust keyword spotting models. We enable on-device learning with less than 10 kB of memory, using only 100 labeled utterances to recover 5% accuracy after adapting to the complex speech noise. We demonstrate that domain adaptation can be achieved on ultra-low-power microcontrollers with as little as 806 mJ in only 14 s on always-on, battery-operated devices.
Two-sided Acoustic Metascreen for Broadband and Individual Reflection and Transmission Control
Authors: Ao Chen, Xin Zhang
Date: 2024-03-12 00:00:00

Acoustic wave modulation plays a pivotal role in various applications, including sound-field reconstruction, wireless communication, and particle manipulation, among others. However, current acoustic metamaterial and metasurface designs typically focus on controlling either reflection or transmission waves, often overlooking the coupling between amplitude and phase of acoustic waves. To fulfill this gap, we propose and experimentally validate a design enabling complete control of reflected and transmitted acoustic waves individually across a frequency range of 4 kHz to 8 kHz, allowing arbitrary combinations of amplitude and phase for reflected and transmitted sound in a broadband manner. Additionally, we demonstrate the significance of our approach for sound manipulation by achieving acoustic diffusion, reflection, focusing, and generating a two-sided 3D hologram at three distinct frequencies. These findings open an alternative avenue for extensively engineering sound waves, promising applications in acoustics and related fields.
Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
Authors: Moonjeong Park, Jaeseung Heo, Dongwoo Kim
Date: 2024-03-11 00:00:00
Tasks: Graph Neural Network

Graph Neural Network (GNN) resembles the diffusion process, leading to the over-smoothing of learned representations when stacking many layers. Hence, the reverse process of message passing can produce the distinguishable node representations by inverting the forward message propagation. The distinguishable representations can help us to better classify neighboring nodes with different labels, such as in heterophilic graphs. In this work, we apply the design principle of the reverse process to the three variants of the GNNs. Through the experiments on heterophilic graph data, where adjacent nodes need to have different representations for successful classification, we show that the reverse process significantly improves the prediction performance in many cases. Additional analysis reveals that the reverse mechanism can mitigate the over-smoothing over hundreds of layers. Our code is available at https://github.com/ml-postech/reverse-gnn.
Topology Data Analysis-based Error Detection for Semantic Image Transmission with Incremental Knowledge-based HARQ
Authors: Fei Ni, Rongpeng Li, Zhifeng Zhao, Honggang Zhang
Date: 2024-03-18 00:00:00
Tasks: Semantic Communication, Topological Data Analysis

Semantic communication (SemCom) aims to achieve high fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy. Nevertheless, semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism (e.g., hybrid automatic repeat request [HARQ]) is indispensable. In that regard, instead of discarding previously transmitted information, the incremental knowledge-based HARQ (IK-HARQ) is deemed as a more effective mechanism that could sufficiently utilize the information semantics. However, considering the possible existence of semantic ambiguity in image transmission, a simple bit-level cyclic redundancy check (CRC) might compromise the performance of IK-HARQ. Therefore, it emerges a strong incentive to revolutionize the CRC mechanism, so as to reap the benefits of both SemCom and HARQ. In this paper, built on top of swin transformer-based joint source-channel coding (JSCC) and IK-HARQ, we propose a semantic image transmission framework SC-TDA-HARQ. In particular, different from the conventional CRC, we introduce a topological data analysis (TDA)-based error detection method, which capably digs out the inner topological and geometric information of images, so as to capture semantic information and determine the necessity for re-transmission. Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework, especially under the limited bandwidth condition, and manifest the superiority of TDA-based error detection method in image transmission.
The Role of Extended Horizon Methodology in Renewable-Dense Grids With Inter-Day Long-Duration Energy Storage
Authors: Amogh A. Thatte, Sourabh Dalvi, Vincent Carag, Jiazi Zhang, Jennie Jorgenson, Omar J. Guerra
Date: 2024-03-18 00:00:00
Tasks: Computational Efficiency

This study addresses the challenges in optimizing long-duration energy storage (LDES) dispatch within future power systems featuring high integration of variable renewable energy (VRE). The research focuses on conducting a comparative analysis between traditional and extended horizon methods for the optimization of LDES dispatch, using open-source and commercial production cost models (PCMs), tested on a futuristic Electric Reliability Council of Texas (ERCOT) grid. The findings indicate that, despite its complexity and longer solution times, the extended horizon approach demonstrates superior performance in LDES dispatch and effectively reduces the impact of degenerate solutions in sequential simulations. This study underscores the trade-offs between computational efficiency and improvement in storage dispatch, which is crucial for future energy systems. The analysis highlights the necessity of addressing the degeneracy issue in storage dispatch in grids dominated by zero operating cost VRE generators and low operating cost energy storage devices. Additionally, the research reveals revenue discrepancies for LDES operators across different models, a consequence of the persistent presence of degeneracy in high VRE systems. These findings suggest an urgent need for refined modeling techniques in the planning and operation of future energy systems.
Deep Neural Network NMPC for Computationally Tractable Optimal Power Management of Hybrid Electric Vehicle
Authors: Suyong Park, Duc Giap Nguyen, Jinrak Park, Dohee Kim, Jeong Soo Eo, Kyoungseok Han
Date: 2024-03-17 00:00:00
Tasks: energy management, Management, Model Predictive Control

This study presents a method for deep neural network nonlinear model predictive control (DNN-MPC) to reduce computational complexity, and we show its practical utility through its application in optimizing the energy management of hybrid electric vehicles (HEVs). For optimal power management of HEVs, we first design the online NMPC to collect the data set, and the deep neural network is trained to approximate the NMPC solutions. We assess the effectiveness of our approach by conducting comparative simulations with rule and online NMPC-based power management strategies for HEV, evaluating both fuel consumption and computational complexity. Lastly, we verify the real-time feasibility of our approach through process-in-the-loop (PIL) testing. The test results demonstrate that the proposed method closely approximates the NMPC performance while substantially reducing the computational burden.
Air Traffic Management for Collaborative Routing of Unmanned Aerial Vehicles via Potential Fields
Authors: Josue N. Rivera, Dengfeng Sun
Date: 2024-03-17 00:00:00
Tasks: Management

Aerial cargo transport is anticipated to play a pivotal role in the distribution of goods within urban environments. The shift is propelled by the surge in e-commerce, the imperative to deliver essential supplies to isolated areas, and the growing demand for expedited and more accessible deliveries. Our research introduces a quantifiable standard for defining routing restrictions for Unmanned Aircraft System Traffic Management (UTM) using the concept of repulsive potential fields. Furthermore, we propose a scalable infrastructure that facilitates collaborative routing of cargo Unmanned Aerial Vehicles (UAVs) by independent shareholders. The practicality of the infrastructure is validated through a functional prototype implemented at a national scale.
Unbiased Parameter Estimation via DREM with Annihilators
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2024-03-17 00:00:00
Tasks: parameter estimation

In adaptive control theory, the dynamic regressor extension and mixing (DREM) procedure has become widespread as it allows one to describe major of adaptive control problems in unified terms of the parameter estimation problem of a regression equation with a scalar regressor. However, when the system/parameterization is affected by perturbations, the estimation laws, which are designed on the basis of such equation, asymptotically provides only biased estimates. In this paper, based on the bias-eliminated least-squares (BELS) approach, a modification of DREM procedure is proposed to annihilate perturbations asymptotically and, consequently, asymptotically obtain unbiased estimates. The theoretical results are supported with mathematical modelling and can be used to design adaptive observers and control systems.
Unsupervised Learning for Equitable DER Control
Authors: Zhenyi Yuan, Guido Cavraro, Ahmed S. Zamzam, Jorge Cortés
Date: 2024-03-17 00:00:00
Tasks: Fairness

In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers. We propose an unsupervised learning framework to train functions that can closely approximate optimal power flow (OPF) solutions. The primary aim is to establish specific conditions under which these learned functions can collectively guide the network towards desired configurations asymptotically, leveraging an incremental control approach. The flexibility of the proposed methodology allows to integrate fairness-driven components into the cost function associated with the OPF problem. This addition seeks to mitigate power curtailment disparities among DERs, thereby promoting equitable power injections across the network. To demonstrate the effectiveness of the proposed approach, power flow simulations are conducted using the IEEE 37-bus feeder. The findings not only showcase the guaranteed system stability but also underscore its improved overall performance.
Signal Fidelity in Degenerate and Nondegenerate Mode Parametric Amplifier Receiving Antennas
Authors: Clayton Blosser, Adrian Bauer, Jessica E. Ruyle, K. C. Kerby-Patel, Kurt Schab
Date: 2024-03-17 00:00:00

The gain, received power bandwidth, transient characteristics, and signal fidelity of two time-varying electrically small antennas based on parametric amplifier design are studied using practical QAM signals. Results show that interference from the difference harmonic present in the response of degenerate-mode parametric amplification decreases its signal throughput relative to a reference linear time-invariant (LTI) receiver, despite its apparent increased received power bandwidth in the frequency domain. The analysis also demonstrates that a non-degenerate parametric receiver, lacking this detrimental effect, exhibits increased signal throughput over the reference LTI receiver.
Stochastic Lp string stability analysis in predecessor-following platoons under packet losses
Authors: Alejandro I. Maass, Francisco J. Vargas, Andres A. Peters, Juan I. Yuz
Date: 2024-03-16 00:00:00
Tasks: Scheduling

In this paper, we study (homogeneous) predecessor-following platoons in which the vehicle-to-vehicle (V2V) communications are affected by random packet losses. We model the overall platoon as a stochastic hybrid system and analyse its string stability via a small-gain approach. For nonlinear platoons, we illustrate how the different elements of the platoon have an impact on string stability, such as platoon topology and vehicle scheduling. For linear time-invariant platoons, we provide an explicit string stability condition that illustrates the interplay between the channel success probability, transmission rate, and time headway constant. Lastly, we illustrate our results by numerical simulations.
MPC for Tracking applied to rendezvous with non-cooperative tumbling targets ensuring stability and feasibility
Authors: Jose Antonio Rebollo, Rafael Vazquez, Ignacio Alvarado, Daniel Limon
Date: 2024-03-16 00:00:00

A Model Predictive Controller for Tracking is introduced for rendezvous with non-cooperative tumbling targets in active debris removal applications. The target's three-dimensional non-periodic rotational dynamics as well as other state and control constraints are considered. The approach is based on applying an intermediate coordinate transformation that eliminates the time-dependency due to rotations in the constraints. The control law is then found as the solution to a QP problem with linear constraints and dynamics, as derived from the HCW equations, that provides feasibility and stability guarantees by means of a terminal LQR and dead-beat region. The proposed control algorithm performs well in a realistic simulation scenario, namely a near rendezvous with the Envisat spacecraft.
Firm-Dispatchable Power and its Requirement in a Power System based on Variable Generation
Authors: Stephen R. Clark, Craig McGregor
Date: 2024-03-16 00:00:00

Many countries have commenced a transition from fossil fuel-based electricity generation systems to sustainable systems based on wind and solar generation. It is often noted that the least cost approach would involve a massive scale-up in the building of variable renewables, supported by battery storage and gas peaking plants. The required backup should be firm-dispatchable generation rather than peaking power. The wind and solar generation aspects for this system are clearly defined and understood, however, the term firm-dispatchable power is not defined and the specific requirements are poorly understood. This study seeks to define firm-dispatchable power in this context and its requirement in the sustainable generation system. The study compares 100% renewable generation scenarios from South Africa, Texas, and the UK to demonstrate the requirement for this firm-dispatchable generation. The results indicate that firm-dispatchable generation must be available to replace the renewable generation completely. The required installed capacity for this firm-dispatchable generation does not vary with the distinct demand profiles of the different locations or their comparative renewable generation profiles. It also does not change significantly with the use of energy storage. The usage for this firm-dispatchable generation will vary due to the comparative economics of its use, but the requirement for its installation does not change.
Extended Kalman Filtering for Recursive Online Discrete-Time Inverse Optimal Control
Authors: Tian Zhao, Timothy L. Molloy
Date: 2024-03-16 00:00:00

We formulate the discrete-time inverse optimal control problem of inferring unknown parameters in the objective function of an optimal control problem from measurements of optimal states and controls as a nonlinear filtering problem. This formulation enables us to propose a novel extended Kalman filter (EKF) for solving inverse optimal control problems in a computationally efficient recursive online manner that requires only a single pass through the measurement data. Importantly, we show that the Jacobians required to implement our EKF can be computed efficiently by exploiting recent Pontryagin differentiable programming results, and that our consideration of an EKF enables the development of first-of-their-kind theoretical error guarantees for online inverse optimal control with noisy incomplete measurements. Our proposed EKF is shown to be significantly faster than an alternative unscented Kalman filter-based approach.
Time-Robust Path Planning with Piece-Wise Linear Trajectory for Signal Temporal Logic Specifications
Authors: Nhan-Khanh Le, Erfaun Noorani, Sandra Hirche, John Baras
Date: 2024-03-15 00:00:00

Real-world scenarios are characterized by timing uncertainties, e.g., delays, and disturbances. Algorithms with temporal robustness are crucial in guaranteeing the successful execution of tasks and missions in such scenarios. We study time-robust path planning for synthesizing robots' trajectories that adhere to spatial-temporal specifications expressed in Signal Temporal Logic (STL). In contrast to prior approaches that rely on {discretize}d trajectories with fixed time steps, we leverage Piece-Wise Linear (PWL) signals for the synthesis. PWL signals represent a trajectory through a sequence of time-stamped waypoints. This allows us to encode the STL formula into a Mixed-Integer Linear Program (MILP) with fewer variables. This reduction is more pronounced for specifications with a long planning horizon. To that end, we define time-robustness for PWL signals. Subsequently, we propose quantitative semantics for PWL signals according to the recursive syntax of STL and prove their soundness. We then propose an encoding strategy to transform our semantics into a MILP. Our simulations showcase the soundness and the performance of our algorithm.
Symmetry-Guided Reinforcement Learning for Versatile Quadrupedal Gait Generation
Authors: Jiayu Ding, Xulin Chen, Garret E. Katz, Zhenyu Gan
Date: 2024-03-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Quadrupedal robots exhibit a wide range of viable gaits, but generating specific footfall sequences often requires laborious expert tuning of numerous variables, such as touch-down and lift-off events and holonomic constraints for each leg. This paper presents a unified reinforcement learning framework for generating versatile quadrupedal gaits by leveraging the intrinsic symmetries of dynamic legged systems. We propose a symmetry-guided reward function design that incorporates temporal, morphological, and time-reversal symmetries, streamlining gait design, accelerating learning, and enhancing robot agility. By focusing on preserved symmetries and natural dynamics, our approach eliminates the need for predefined trajectories or footfall sequences, enabling smooth transitions between diverse locomotion patterns such as trotting, bounding, half-bounding, and galloping. Implemented on the Petoi Bittle robot, our method demonstrates robust performance across a range of speeds in both simulations and hardware tests, significantly improving gait adaptability without extensive reward tuning or explicit foot placement control. This work provides insights into dynamic locomotion strategies and underscores the crucial role of symmetries in robotic gait design.