pwc-archive/papers-with-abstracts huggingface dataset:
Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems
Authors: Mingming Zhao, Lin Liu, Lifu Liu, Mengke Li, Qi Tian
Date: 2023-06-08 00:00:00
Tasks: Decoder, Denoising

The downlink channel state information (CSI) estimation and low overhead acquisition are the major challenges for massive MIMO systems in frequency division duplex to enable high MIMO gain. Recently, numerous studies have been conducted to harness the power of deep neural networks for better channel estimation and feedback. However, existing methods have yet to fully exploit the intrinsic correlation features present in CSI. As a consequence, distinct network structures are utilized for handling these two tasks separately. To achieve joint channel estimation and feedback, this paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. To effectively capture and restructure correlation features, a self-mask-attention coding is proposed, complemented by an active masking strategy designed to improve efficiency. The channel estimation is achieved through the decoder part, wherein a lightweight multilayer perceptron denoising module is utilized for further accurate estimation. Extensive experiments demonstrate that our method not only outperforms state-of-the-art channel estimation and feedback techniques in joint tasks but also achieves beneficial performance in individual tasks.
iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning
Authors: Xiyang Wu, Rohan Chandra, Tianrui Guan, Amrit Singh Bedi, Dinesh Manocha
Date: 2023-06-09 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance, Decision Making, Motion Planning, Multi-agent Reinforcement Learning

Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of nearby drivers. In this work, we introduce a distributed multi-agent reinforcement learning (MARL) algorithm that can predict trajectories and intents in dense and heterogeneous traffic scenarios. Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations. We model two distinct incentives for agents' strategies: Behavioral Incentive for high-level decision-making based on their driving behavior or personality and Instant Incentive for motion planning for collision avoidance based on the current traffic state. Our approach enables agents to infer their opponents' behavior incentives and integrate this inferred information into their decision-making and motion-planning processes. We perform experiments on two simulation environments, Non-Cooperative Navigation and Heterogeneous Highway. In Heterogeneous Highway, results show that, compared with centralized training decentralized execution (CTDE) MARL baselines such as QMIX and MAPPO, our method yields a 4.3% and 38.4% higher episodic reward in mild and chaotic traffic, with 48.1% higher success rate and 80.6% longer survival time in chaotic traffic. We also compare with a decentralized training decentralized execution (DTDE) baseline IPPO and demonstrate a higher episodic reward of 12.7% and 6.3% in mild traffic and chaotic traffic, 25.3% higher success rate, and 13.7% longer survival time.
How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations
Authors: Franck Djeumou, Cyrus Neary, Ufuk Topcu
Date: 2023-06-10 00:00:00
Tasks: Inductive Bias, Model-based Reinforcement Learning, Model Predictive Control

We present a framework and algorithms to learn controlled dynamics models using neural stochastic differential equations (SDEs) -- SDEs whose drift and diffusion terms are both parametrized by neural networks. We construct the drift term to leverage a priori physics knowledge as inductive bias, and we design the diffusion term to represent a distance-aware estimate of the uncertainty in the learned model's predictions -- it matches the system's underlying stochasticity when evaluated on states near those from the training dataset, and it predicts highly stochastic dynamics when evaluated on states beyond the training regime. The proposed neural SDEs can be evaluated quickly enough for use in model predictive control algorithms, or they can be used as simulators for model-based reinforcement learning. Furthermore, they make accurate predictions over long time horizons, even when trained on small datasets that cover limited regions of the state space. We demonstrate these capabilities through experiments on simulated robotic systems, as well as by using them to model and control a hexacopter's flight dynamics: A neural SDE trained using only three minutes of manually collected flight data results in a model-based control policy that accurately tracks aggressive trajectories that push the hexacopter's velocity and Euler angles to nearly double the maximum values observed in the training dataset.
Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation
Authors: Jinzi Qi, Hugo Van hamme
Date: 2023-06-12 00:00:00
Tasks: Diversity, speech-recognition, Speech Recognition, Transfer Learning

In dysarthric speech recognition, data scarcity and the vast diversity between dysarthric speakers pose significant challenges. While finetuning has been a popular solution, it can lead to overfitting and low parameter efficiency. Adapter modules offer a better solution, with their small size and easy applicability. Additionally, Adapter Fusion can facilitate knowledge transfer from multiple learned adapters, but may employ more parameters. In this work, we apply Adapter Fusion for target speaker adaptation and speech recognition, achieving acceptable accuracy with significantly fewer speaker-specific trainable parameters than classical finetuning methods. We further improve the parameter efficiency of the fusion layer by reducing the size of query and key layers and using Householder transformation to reparameterize the value linear layer. Our proposed fusion layer achieves comparable recognition results to the original method with only one third of the parameters.
Audio-Visual Speech Enhancement With Selective Off-Screen Speech Extraction
Authors: Tomoya Yoshinaga, Keitaro Tanaka, Shigeo Morishima
Date: 2023-06-10 00:00:00
Tasks: Computational Efficiency, Speech Enhancement, Speech Extraction

This paper describes an audio-visual speech enhancement (AV-SE) method that estimates from noisy input audio a mixture of the speech of the speaker appearing in an input video (on-screen target speech) and of a selected speaker not appearing in the video (off-screen target speech). Although conventional AV-SE methods have suppressed all off-screen sounds, it is necessary to listen to a specific pre-known speaker's speech (e.g., family member's voice and announcements in stations) in future applications of AV-SE (e.g., hearing aids), even when users' sight does not capture the speaker. To overcome this limitation, we extract a visual clue for the on-screen target speech from the input video and a voiceprint clue for the off-screen one from a pre-recorded speech of the speaker. Two clues from different domains are integrated as an audio-visual clue, and the proposed model directly estimates the target mixture. To improve the estimation accuracy, we introduce a temporal attention mechanism for the voiceprint clue and propose a training strategy called the muting strategy. Experimental results show that our method outperforms a baseline method that uses the state-of-the-art AV-SE and speaker extraction methods individually in terms of estimation accuracy and computational efficiency.
Semi-supervsied Learning-based Sound Event Detection using Freuqency Dynamic Convolution with Large Kernel Attention for DCASE Challenge 2023 Task 4
Authors: Ji Won Kim, Sang Won Son, Yoonah Song, Hong Kook Kim, Il Hoon Song, Jeong Eun Lim
Date: 2023-06-10 00:00:00
Tasks: Event Detection, Pseudo Label, Sound Event Detection

This report proposes a frequency dynamic convolution (FDY) with a large kernel attention (LKA)-convolutional recurrent neural network (CRNN) with a pre-trained bidirectional encoder representation from audio transformers (BEATs) embedding-based sound event detection (SED) model that employs a mean-teacher and pseudo-label approach to address the challenge of limited labeled data for DCASE 2023 Task 4. The proposed FDY with LKA integrates the FDY and LKA module to effectively capture time-frequency patterns, long-term dependencies, and high-level semantic information in audio signals. The proposed FDY with LKA-CRNN with a BEATs embedding network is initially trained on the entire DCASE 2023 Task 4 dataset using the mean-teacher approach, generating pseudo-labels for weakly labeled, unlabeled, and the AudioSet. Subsequently, the proposed SED model is retrained using the same pseudo-label approach. A subset of these models is selected for submission, demonstrating superior F1-scores and polyphonic SED score performance on the DCASE 2023 Challenge Task 4 validation dataset.
DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models
Authors: Nikolaos Louloudakis, Perry Gibson, José Cano, Ajitha Rajan
Date: 2023-06-05 00:00:00
Tasks: Autonomous Driving, Deep Learning

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as the computational environment. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood. In this paper we present a differential testing framework, DeltaNN, that allows us to assess the impact of different computational environment parameters on the performance of image recognition models during deployment, post training. DeltaNN generates different implementations of a given image recognition model for variations in environment parameters, namely, deep learning frameworks, compiler optimizations and hardware devices and analyzes differences in model performance as a result. Using DeltaNN, we conduct an empirical study of robustness analysis of three popular image recognition models using the ImageNet dataset. We report the impact in terms of misclassifications and inference time differences across different settings. In total, we observed up to 100% output label differences across deep learning frameworks, and up to 81% unexpected performance degradation in terms of inference time, when applying compiler optimizations.
Enlarging Stability Region of Urban Networks with Imminent Supply Prediction
Authors: DianChao Lin, Li Li
Date: 2023-06-12 00:00:00
Tasks: Decision Making

Stability region is a key index to characterize a dynamic processing system's ability to handle incoming demands. It is a multidimensional space when the system has multiple OD pairs where their service rates interact. Urban traffic network is such a system. Traffic congestion appears when its demand approaches or exceeds the upper frontier of its stability region. In this decade, with the rapid development of traffic sense technology, real-time traffic operations, e.g., BackPressure (BP) control, have gained lots of research attention. Urban network's mobility could be further improved with these timely demand-responding strategies. However, most studies on real-time controls continue with traditional supply assumptions and ignore an important fact -- imminent saturation flow rate (I-SFR), i.e., the system's real-time service rate under green, is neither fixed nor given, but hard to be precisely known. It is unknown how the knowledge level of I-SFR would influence the stability region. This paper proves that knowing more accurate I-SFR can enlarge the upper frontier of the network's stability region. Furthermore, BP policy with predicted I-SFR can stabilize the network within the enlarged stability region and relieve the congestion level of the traffic network. Therefore, improving the I-SFR's prediction accuracy is meaningful for traffic operations.
Koopman-inspired Implicit Backward Reachable Sets for Unknown Nonlinear Systems
Authors: Haldun Balim, Antoine Aspeel, Zexiang Liu, Necmiye Ozay
Date: 2023-06-12 00:00:00

Koopman liftings have been successfully used to learn high dimensional linear approximations for autonomous systems for prediction purposes, or for control systems for leveraging linear control techniques to control nonlinear dynamics. In this paper, we show how learned Koopman approximations can be used for state-feedback correct-by-construction control. To this end, we introduce the Koopman over-approximation, a (possibly hybrid) lifted representation that has a simulation-like relation with the underlying dynamics. Then, we prove how successive application of controlled predecessor operation in the lifted space leads to an implicit backward reachable set for the actual dynamics. Finally, we demonstrate the approach on two nonlinear control examples with unknown dynamics.
Transient Stability Analysis of Grid-Connected Converters Based on Reverse-Time Trajectory
Authors: Mohammad Kazem Bakhshizadeh, Sujay Ghosh, Guangya Yang, Łukasz Kocewiak
Date: 2023-06-12 00:00:00

As the proportion of converter-interfaced renewable energy resources in the power system is increasing, the strength of the power grid at the connection point of wind turbine generators (WTGs) is gradually weakening. Existing research has shown that when connected with the weak grid, the dynamic characteristics of the traditional grid-following controlled converters will deteriorate, and unstable phenomena such as oscillation are prone to arise. Due to the limitations of linear analysis that can not sufficiently capture the stability phenomena, transient stability must also be investigated. So far, standalone time-domain simulations or analytical Lyapunov stability criteria have been used to investigate transient stability. However, time-domain simulations have proven to be computationally too heavy, while analytical methods are more complex to formulate, require many assumptions, and are conservative. This paper demonstrates an innovative approach to estimating the system boundaries via hybrid - linearised Lyapunov function-based approach and the time-reversal technique. The proposed methodology enables compensation for both time-consuming simulations and the conservative nature of Lyapunov functions. This work brings out the clear distinction between the system boundaries with different post-fault active current ramp rate controls. At the same time providing a new perspective on critical clearing times for wind turbine systems. Finally, the stability boundary is verified using time domain simulation studies.
Single-Integrator Consensus Dynamics over Minimally Reactive Networks
Authors: Amirhossein Nazerian, David Phillips, Hernan A. Makse, Francesco Sorrentino
Date: 2023-06-12 00:00:00

The problem of achieving consensus in a network of connected systems arises in many science and engineering applications. In contrast to previous works, we focus on the system reactivity, i.e., the initial amplification of the norm of the system states. We identify a class of networks that we call minimally reactive, which are such that the indegree and the outdegree of each node of the network are the same. We propose several optimization procedures in which minimum perturbations (links or link weights) are imposed on a given network topology to make it minimally reactive. A new concept of structural reactivity is introduced which measures how much a given network is far from becoming minimally reactive by link perturbations. The structural reactivity of directed random graphs is studied.
Fractional Barrier Lyapunov Functions with Application to Learning Control
Authors: Mingxuan Sun
Date: 2023-06-11 00:00:00

Barrier Lyapunov functions are suitable for learning control designs, due to their feature of finite duration tracking. This paper presents fractional barrier Lyapunov functions, provided and compared with the conventional ones in the error-constraint learning control designs. Two error models are adopted and the desired compensation control approach is applied for a non-parametric design, allowing two kinds of uncertainties involved in the error dynamics. Theoretical results about existence of the solution and convergence of the learning control schemes are presented. It is shown that fully-saturated learning algorithms play important role in assuring boundedness of the estimates, by which the error constraint objective can be achieved. Moreover, the robust technique is developed through modifying the discontinuous action involved in the learning control scheme that yields the expected tracking performance in the presence of residual.
Use of Robust DOB/CDOB Compensation to Improve Autonomous Vehicle Path Following Performance in the Presence of Model Uncertainty, CAN Bus Delays and External Disturbances
Authors: Haoan Wang, Levent Guvenc
Date: 2023-06-10 00:00:00
Tasks: Friction

A path tracking control system is chosen as the proof-of-concept demonstration application in this paper. A disturbance observer (DOB) is embedded within the steering to path error automated driving loop to handle uncertain parameters such as vehicle mass, vehicle velocities and road friction coefficient and to reject yaw moment disturbances. The compensation of vehicle model with the embedded disturbance observer forces it to behave like its nominal model within the bandwidth of the disturbance observer. A parameter space approach based steering controller is then used to optimize performance. The proposed method demonstrates good disturbance rejection and achieves stability robustness. The variable time delay from the steer-by-wire system in an actual vehicle can also lead to stability issues since it adds large negative phase angle to the plant frequency response and tends to destabilize it. A communication disturbance observer (CDOB) based time delay compensation approach that does not require exact knowledge of this time delay is embedded into the steering actuation loop to handle this problem. Stability analysis of both DOB and CDOB compensation system are presented in this paper. Extensive model-in-the-loop simulations were performed to test the designed disturbance observer and CDOB systems and show reduced path following errors in the presence of uncertainty, disturbances and time delay. A validated model of our 2017 Ford Fusion Hybrid research autonomous vehicle is used in the simulation analyses. Simulation results verify the performance enhancement of the vehicle path following control with proposed DOB and CDOB structure. A HiL simulator that uses a validated CarSim model with sensors and traffic will be used later to verify the real time capability of our approach.
A Chance-Constrained Optimal Design of Volt/VAR Control Rules for Distributed Energy Resources
Authors: Jinlei Wei, Sarthak Gupta, Dionysios C. Aliprantis, Vassilis Kekatos
Date: 2023-06-10 00:00:00

Deciding setpoints for distributed energy resources (DERs) via local control rules rather than centralized optimization offers significant autonomy. The IEEE Standard 1547 recommends deciding DER setpoints using Volt/VAR rules. Although such rules are specified as non-increasing piecewise-affine, their exact shape is left for the utility operators to decide and possibly customize per bus and grid conditions. To address this need, this work optimally designs Volt/VAR rules to minimize ohmic losses on lines while maintaining voltages within allowable limits. This is practically relevant as excessive reactive injections could reduce equipment's lifetime due to overloading. We consider a linearized single-phase grid model. Even under this setting, optimal rule design (ORD) is technically challenging as Volt/VAR rules entail mixed-integer models, stability implications, and uncertainties in grid loading. Uncertainty is handled by minimizing the average losses under voltage chance constraints. To cope with the piecewise-affine shape of the rules, we build upon our previous reformulation of ORD as a deep learning task. A recursive neural network (RNN) surrogates Volt/VAR dynamics and thanks to back-propagation, we expedite this chance-constrained ORD. RNN weights coincide with rule parameters, and are trained using primal-dual decomposition. Numerical tests corroborate the efficacy of this novel ORD formulation and solution methodology.
Autonomous Drifting with 3 Minutes of Data via Learned Tire Models
Authors: Franck Djeumou, Jonathan Y. M. Goh, Ufuk Topcu, Avinash Balachandran
Date: 2023-06-10 00:00:00
Tasks: Model Predictive Control

Near the limits of adhesion, the forces generated by a tire are nonlinear and intricately coupled. Efficient and accurate modelling in this region could improve safety, especially in emergency situations where high forces are required. To this end, we propose a novel family of tire force models based on neural ordinary differential equations and a neural-ExpTanh parameterization. These models are designed to satisfy physically insightful assumptions while also having sufficient fidelity to capture higher-order effects directly from vehicle state measurements. They are used as drop-in replacements for an analytical brush tire model in an existing nonlinear model predictive control framework. Experiments with a customized Toyota Supra show that scarce amounts of driving data -- less than three minutes -- is sufficient to achieve high-performance autonomous drifting on various trajectories with speeds up to 45mph. Comparisons with the benchmark model show a $4 \times$ improvement in tracking performance, smoother control inputs, and faster and more consistent computation time.
Sensing-Aided Peer-to-Peer Millimeter-Wave Communication
Authors: Xiangyu Li, Sidong Guo, Shez Malik
Date: 2023-06-09 00:00:00
Tasks: Scheduling

One of the bottlenecks of modern communications is to enable sensing and mutual communication simultaneously without causing scheduling conflicts, and how sensing may be leveraged to help directional communication accuracy. To this end, we propose and implement a novel peer-to-peer (P2P) millimeter-wave communication system to jointly achieve beamforming and sensing. A radar and IMU-assisted tracking and beamforming algorithm is designed and tested. The results show that a robust tracking capacity with an overall higher throughput can be obtained. It is also hopeful that based on our proposed system, our design and implementation can be deployed in a more scalable manner for future extensions.