pwc-archive/papers-with-abstracts huggingface dataset:
Model-free Resilient Controller Design based on Incentive Feedback Stackelberg Game and Q-learning
Authors: Jiajun Shen, Fengjun Li, Morteza Hashemi, Huazhen Fang
Date: 2024-03-13 00:00:00
Tasks: Q-Learning

In the swift evolution of Cyber-Physical Systems (CPSs) within intelligent environments, especially in the industrial domain shaped by Industry 4.0, the surge in development brings forth unprecedented security challenges. This paper explores the intricate security issues of Industrial CPSs (ICPSs), with a specific focus on the unique threats presented by intelligent attackers capable of directly compromising the controller, thereby posing a direct risk to physical security. Within the framework of hierarchical control and incentive feedback Stackelberg game, we design a resilient leading controller (leader) that is adaptive to a compromised following controller (follower) such that the compromised follower acts cooperatively with the leader, aligning its strategies with the leader's objective to achieve a team-optimal solution. First, we provide sufficient conditions for the existence of an incentive Stackelberg solution when system dynamics are known. Then, we propose a Q-learning-based Approximate Dynamic Programming (ADP) approach, and corresponding algorithms for the online resolution of the incentive Stackelberg solution without requiring prior knowledge of system dynamics. Last but not least, we prove the convergence of our approach to the optimum.
A Hybrid Intelligence Method for Argument Mining
Authors: Michiel van der Meer, Enrico Liscio, Catholijn M. Jonker, Aske Plaat, Piek Vossen, Pradeep K. Murukannaiah
Date: 2024-03-11 00:00:00
Tasks: Argument Mining

Large-scale survey tools enable the collection of citizen feedback in opinion corpora. Extracting the key arguments from a large and noisy set of opinions helps in understanding the opinions quickly and accurately. Fully automated methods can extract arguments but (1) require large labeled datasets that induce large annotation costs and (2) work well for known viewpoints, but not for novel points of view. We propose HyEnA, a hybrid (human + AI) method for extracting arguments from opinionated texts, combining the speed of automated processing with the understanding and reasoning capabilities of humans. We evaluate HyEnA on three citizen feedback corpora. We find that, on the one hand, HyEnA achieves higher coverage and precision than a state-of-the-art automated method when compared to a common set of diverse opinions, justifying the need for human insight. On the other hand, HyEnA requires less human effort and does not compromise quality compared to (fully manual) expert analysis, demonstrating the benefit of combining human and artificial intelligence.
Wavenumber Domain Sparse Channel Estimation in Holographic MIMO
Authors: Xufeng Guo, Yuanbin Chen, Ying Wang, Zhaocheng Wang, Zhu Han
Date: 2024-03-17 00:00:00

In this paper, we investigate the sparse channel estimation in holographic multiple-input multiple-output (HMIMO) systems. The conventional angular-domain representation fails to capture the continuous angular power spectrum characterized by the spatially-stationary electromagnetic random field, thus leading to the ambiguous detection of the significant angular power, which is referred to as the power leakage. To tackle this challenge, the HMIMO channel is represented in the wavenumber domain for exploring its cluster-dominated sparsity. Specifically, a finite set of Fourier harmonics acts as a series of sampling probes to encapsulate the integral of the power spectrum over specific angular regions. This technique effectively eliminates power leakage resulting from power mismatches induced by the use of discrete angular-domain probes. Next, the channel estimation problem is recast as a sparse recovery of the significant angular power spectrum over the continuous integration region. We then propose an accompanying graph-cut-based swap expansion (GCSE) algorithm to extract beneficial sparsity inherent in HMIMO channels. Numerical results demonstrate that this wavenumber-domainbased GCSE approach achieves robust performance with rapid convergence.
Near-Field Channel Modeling for Holographic MIMO Communications
Authors: Tierui Gong, Li Wei, Chongwen Huang, George C. Alexandropoulos, Mérouane Debbah, Chau Yuen
Date: 2024-03-14 00:00:00

Empowered by the latest progress on innovative metamaterials/metasurfaces and advanced antenna technologies, holographic multiple-input multiple-output (H-MIMO) emerges as a promising technology to fulfill the extreme goals of the sixth-generation (6G) wireless networks. The antenna arrays utilized in H-MIMO comprise massive (possibly to extreme extent) numbers of antenna elements, densely spaced less than half-a-wavelength and integrated into a compact space, realizing an almost continuous aperture. Thanks to the expected low cost, size, weight, and power consumption, such apertures are expected to be largely fabricated for near-field communications. In addition, the physical features of H-MIMO enable manipulations directly on the electromagnetic (EM) wave domain and spatial multiplexing. To fully leverage this potential, near-field H-MIMO channel modeling, especially from the EM perspective, is of paramount significance. In this article, we overview near-field H-MIMO channel models elaborating on the various modeling categories and respective features, as well as their challenges and evaluation criteria. We also present EM-domain channel models that address the inherit computational and measurement complexities. Finally, the article is concluded with a set of future research directions on the topic.
Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks
Authors: Chenghong Bian, Yulin Shao, Haotian Wu, Emre Ozfatura, Deniz Gunduz
Date: 2024-03-15 00:00:00
Tasks: Image Compression

We introduce deep joint source-channel coding (DeepJSCC) schemes for image transmission over cooperative relay channels. The relay either amplifies-and-forwards its received signal, called DeepJSCC-AF, or leverages neural networks to extract relevant features from its received signal, called DeepJSCC-PF (Process-and-Forward). We consider both half- and full-duplex relays, and propose a novel transformer-based model at the relay. For a half-duplex relay, it is shown that the proposed scheme learns to generate correlated signals at the relay and source to obtain beamforming gains. In the full-duplex case, we introduce a novel block-based transmission strategy, in which the source transmits in blocks, and the relay updates its knowledge about the input signal after each block and generates its own signal. To enhance practicality, a single transformer-based model is used at the relay at each block, together with an adaptive transmission module, which allows the model to seamlessly adapt to different channel qualities and the transmission powers}. Simulation results demonstrate the superior performance of DeepJSCC-PF compared to the state-of-the-art BPG image compression algorithm operating at the maximum achievable rate of conventional decode-and-forward and compress-and-forward protocols, in both half- and full-duplex relay scenarios over AWGN and Rayleigh fading channels.
Decentralizing Coherent Joint Transmission Precoding via Deterministic Equivalents
Authors: Yuhao Liu, Xinyu Bian, Yizhou Xu, Tianqi Hou, Wenjie Wang, Yuyi Mao, Jun Zhang
Date: 2024-03-15 00:00:00

In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with downlink coherent joint transmission. To avoid costly information exchange among the cooperating base stations in a centralized precoding scheme, we propose a decentralized one by considering the power minimization problem. By approximating the inter-cell interference using the deterministic equivalents, this problem is decoupled to sub-problems which are solved in a decentralized manner at different base stations. Simulation results demonstrate the effectiveness of our proposed decentralized precoding scheme, where only 2 ~ 7% more transmit power is needed compared with the optimal centralized precoder.
Safe Planning through Incremental Decomposition of Signal Temporal Logic Specifications
Authors: Parv Kapoor, Eunsuk Kang, Romulo Meira-Goes
Date: 2024-03-13 00:00:00
Tasks: Navigate, Scheduling, Trajectory Planning

Trajectory planning is a critical process that enables autonomous systems to safely navigate complex environments. Signal temporal logic (STL) specifications are an effective way to encode complex temporally extended objectives for trajectory planning in cyber-physical systems (CPS). However, planning from these specifications using existing techniques scale exponentially with the number of nested operators and the horizon of specification. Additionally, performance is exacerbated at runtime due to limited computational budgets and compounding modeling errors. Decomposing a complex specification into smaller subtasks and incrementally planning for them can remedy these issues. In this work, we present a way to decompose STL requirements temporally to improve planning efficiency and performance. The key insight in our work is to encode all specifications as a set of reachability and invariance constraints and scheduling these constraints sequentially at runtime. Our proposed technique outperforms the state-of-the-art trajectory synthesis techniques for both linear and non linear dynamical systems.
Interactive $360^{\circ}$ Video Streaming Using FoV-Adaptive Coding with Temporal Prediction
Authors: Yixiang Mao, Liyang Sun, Yong liu, Yao Wang
Date: 2024-03-17 00:00:00
Tasks: Prediction

For $360^{\circ}$ video streaming, FoV-adaptive coding that allocates more bits for the predicted user's field of view (FoV) is an effective way to maximize the rendered video quality under the limited bandwidth. We develop a low-latency FoV-adaptive coding and streaming system for interactive applications that is robust to bandwidth variations and FoV prediction errors. To minimize the end-to-end delay and yet maximize the coding efficiency, we propose a frame-level FoV-adaptive inter-coding structure. In each frame, regions that are in or near the predicted FoV are coded using temporal and spatial prediction, while a small rotating region is coded with spatial prediction only. This rotating intra region periodically refreshes the entire frame, thereby providing robustness to both FoV prediction errors and frame losses due to transmission errors. The system adapts the sizes and rates of different regions for each video segment to maximize the rendered video quality under the predicted bandwidth constraint. Integrating such frame-level FoV adaptation with temporal prediction is challenging due to the temporal variations of the FoV. We propose novel ways for modeling the influence of FoV dynamics on the quality-rate performance of temporal predictive coding.We further develop LSTM-based machine learning models to predict the user's FoV and network bandwidth.The proposed system is compared with three benchmark systems, using real-world network bandwidth traces and FoV traces, and is shown to significantly improve the rendered video quality, while achieving very low end-to-end delay and low frame-freeze probability.
Bayesian multi-exposure image fusion for robust high dynamic range ptychography
Authors: Shantanu Kodgirwar, Lars Loetgering, Chang Liu, Aleena Joseph, Leona Licht, Daniel S. Penagos Molina, Wilhelm Eschen, Jan Rothhardt, Michael Habeck
Date: 2024-03-17 00:00:00
Tasks: Multi-Exposure Image Fusion, Retrieval

The limited dynamic range of the detector can impede coherent diffractive imaging (CDI) schemes from achieving diffraction-limited resolution. To overcome this limitation, a straightforward approach is to utilize high dynamic range (HDR) imaging through multi-exposure image fusion (MEF). This method involves capturing measurements at different exposure times, spanning from under to overexposure and fusing them into a single HDR image. The conventional MEF technique in ptychography typically involves subtracting the background noise, ignoring the saturated pixels and then merging the acquisitions. However, this approach is inadequate under conditions of low signal-to-noise ratio (SNR). Additionally, variations in illumination intensity significantly affect the phase retrieval process. To address these issues, we propose a Bayesian MEF modeling approach based on a modified Poisson distribution that takes the background and saturation into account. To infer the model parameters, the expectation-maximization (EM) algorithm is employed. As demonstrated with synthetic and experimental data, our approach outperforms the conventional MEF method, offering superior phase retrieval under challenging experimental conditions. This work underscores the significance of robust multi-exposure image fusion for ptychography, particularly in imaging shot-noise-dominated weakly scattering specimens or in cases where access to HDR detectors with high SNR is limited. Furthermore, the applicability of the Bayesian MEF approach extends beyond CDI to any imaging scheme that requires HDR treatment. Given this versatility, we provide the implementation of our algorithm as a Python package.
JaxDecompiler: Redefining Gradient-Informed Software Design
Authors: Pierrick Pochelu
Date: 2024-03-14 00:00:00

Among numerical libraries capable of computing gradient descent optimization, JAX stands out by offering more features, accelerated by an intermediate representation known as Jaxpr language. However, editing the Jaxpr code is not directly possible. This article introduces JaxDecompiler, a tool that transforms any JAX function into an editable Python code, especially useful for editing the JAX function generated by the gradient function. JaxDecompiler simplifies the processes of reverse engineering, understanding, customizing, and interoperability of software developed by JAX. We highlight its capabilities, emphasize its practical applications especially in deep learning and more generally gradient-informed software, and demonstrate that the decompiled code speed performance is similar to the original.
Toward Adaptive Cooperation: Model-Based Shared Control Using LQ-Differential Games
Authors: Balint Varga
Date: 2024-03-17 00:00:00

This paper introduces a novel model-based adaptive shared control to allow for the identification and design challenge for shared-control systems, in which humans and automation share control tasks. The main challenge is the adaptive behavior of the human in such shared control interactions. Consequently, merely identifying human behavior without considering automation is insufficient and often leads to inadequate automation design. Therefore, this paper proposes a novel solution involving online identification of the human and the adaptation of shared control using Linear-Quadratic differential games. The effectiveness of the proposed online adaptation is analyzed in simulations and compared with a non-adaptive shared control from the state of the art. Finally, the proposed approach is tested through human-in-the-loop experiments, highlighting its suitability for real-time applications.
Is Data All That Matters? The Role of Control Frequency for Learning-Based Sampled-Data Control of Uncertain Systems
Authors: Ralf Römer, Lukas Brunke, SiQi Zhou, Angela P. Schoellig
Date: 2024-03-14 00:00:00
Tasks: All, Gaussian Processes

Learning models or control policies from data has become a powerful tool to improve the performance of uncertain systems. While a strong focus has been placed on increasing the amount and quality of data to improve performance, data can never fully eliminate uncertainty, making feedback necessary to ensure stability and performance. We show that the control frequency at which the input is recalculated is a crucial design parameter, yet it has hardly been considered before. We address this gap by combining probabilistic model learning and sampled-data control. We use Gaussian processes (GPs) to learn a continuous-time model and compute a corresponding discrete-time controller. The result is an uncertain sampled-data control system, for which we derive robust stability conditions. We formulate semidefinite programs to compute the minimum control frequency required for stability and to optimize performance. As a result, our approach enables us to study the effect of both control frequency and data on stability and closed-loop performance. We show in numerical simulations of a quadrotor that performance can be improved by increasing either the amount of data or the control frequency, and that we can trade off one for the other. For example, by increasing the control frequency by 33%, we can reduce the number of data points by half while still achieving similar performance.
Synchronisation-Oriented Design Approach for Adaptive Control
Authors: Namhoon Cho, Seokwon Lee, Hyo-Sang Shin
Date: 2024-03-14 00:00:00

This study presents a synchronisation-oriented perspective towards adaptive control which views model-referenced adaptation as synchronisation between actual and virtual dynamic systems. In the context of adaptation, model reference adaptive control methods make the state response of the actual plant follow a reference model. In the context of synchronisation, consensus methods involving diffusive coupling induce a collective behaviour across multiple agents. We draw from the understanding about the two time-scale nature of synchronisation motivated by the study of blended dynamics. The synchronisation-oriented approach consists in the design of a coupling input to achieve desired closed-loop error dynamics followed by the input allocation process to shape the collective behaviour. We suggest that synchronisation can be a reasonable design principle allowing a more holistic and systematic approach to the design of adaptive control systems for improved transient characteristics. Most notably, the proposed approach enables not only constructive derivation but also substantial generalisation of the previously developed closed-loop reference model adaptive control method. Practical significance of the proposed generalisation lies at the capability to improve the transient response characteristics and mitigate the unwanted peaking phenomenon at the same time.
Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI
Authors: Dong Shu, Zhouyao Zhu
Date: 2024-03-14 00:00:00
Tasks: Autonomous Vehicles, Decision Making

This report investigates the history and impact of Generative Models and Connected and Automated Vehicles (CAVs), two groundbreaking forces pushing progress in technology and transportation. By focusing on the application of generative models within the context of CAVs, the study aims to unravel how this integration could enhance predictive modeling, simulation accuracy, and decision-making processes in autonomous vehicles. This thesis discusses the benefits and challenges of integrating generative models and CAV technology in transportation. It aims to highlight the progress made, the remaining obstacles, and the potential for advancements in safety and innovation.
Rollover Prevention for Mobile Robots with Control Barrier Functions: Differentiator-Based Adaptation and Projection-to-State Safety
Authors: Ersin Das, Aaron D. Ames, Joel W. Burdick
Date: 2024-03-13 00:00:00

This paper develops rollover prevention guarantees for mobile robots using control barrier function (CBF) theory, and demonstrates the method experimentally. We consider a safety measure based on a zero moment point condition through the lens of CBFs. However, these conditions depend on time-varying and noisy parameters. To address this issue, we present a differentiator-based safety-critical controller that estimates these parameters and pairs Input-to-State Stable (ISS) differentiator dynamics with CBFs to achieve rigorous safety guarantees. Additionally, to ensure safety in the presence of disturbances, we utilize a time-varying extension of Projection-to-State Safety (PSSf). The effectiveness of the proposed method is demonstrated via experiments on a tracked robot with a rollover potential on steep slopes.
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer
Authors: Kenta Tsukahara, Kanji Tanaka, Daiki Iwata
Date: 2024-03-13 00:00:00
Tasks: Continual Learning, Image Retrieval, Knowledge Distillation, Transfer Learning

A typical assumption in state-of-the-art self-localization models is that an annotated training dataset is available in the target workspace. However, this does not always hold when a robot travels in a general open-world. This study introduces a novel training scheme for open-world distributed robot systems. In our scheme, a robot ("student") can ask the other robots it meets at unfamiliar places ("teachers") for guidance. Specifically, a pseudo-training dataset is reconstructed from the teacher model and thereafter used for continual learning of the student model. Unlike typical knowledge transfer schemes, our scheme introduces only minimal assumptions on the teacher model, such that it can handle various types of open-set teachers, including uncooperative, untrainable (e.g., image retrieval engines), and blackbox teachers (i.e., data privacy). Rather than relying on the availability of private data of teachers as in existing methods, we propose to exploit an assumption that holds universally in self-localization tasks: "The teacher model is a self-localization system" and to reuse the self-localization system of a teacher as a sole accessible communication channel. We particularly focus on designing an excellent student/questioner whose interactions with teachers can yield effective question-and-answer sequences that can be used as pseudo-training datasets for the student self-localization model. When applied to a generic recursive knowledge distillation scenario, our approach exhibited stable and consistent performance improvement.
Fine-Grained Engine Fault Sound Event Detection Using Multimodal Signals
Authors: Dennis Fedorishin, Livio Forte III, Philip Schneider, Srirangaraj Setlur, Venu Govindaraju
Date: 2024-03-16 00:00:00
Tasks: Event Detection, Fault Detection, Sound Event Detection

Sound event detection (SED) is an active area of audio research that aims to detect the temporal occurrence of sounds. In this paper, we apply SED to engine fault detection by introducing a multimodal SED framework that detects fine-grained engine faults of automobile engines using audio and accelerometer-recorded vibration. We first introduce the problem of engine fault SED on a dataset collected from a large variety of vehicles with expertly-labeled engine fault sound events. Next, we propose a SED model to temporally detect ten fine-grained engine faults that occur within vehicle engines and further explore a pretraining strategy using a large-scale weakly-labeled engine fault dataset. Through multiple evaluations, we show our proposed framework is able to effectively detect engine fault sound events. Finally, we investigate the interaction and characteristics of each modality and show that fusing features from audio and vibration improves overall engine fault SED capabilities.