pwc-archive/papers-with-abstracts huggingface dataset:
AdaProj: Adaptively Scaled Angular Margin Subspace Projections for Anomalous Sound Detection with Auxiliary Classification Tasks
Authors: Kevin Wilkinghoff
Date: 2024-03-21 00:00:00
Tasks: Self-Supervised Learning

The state-of-the-art approach for semi-supervised anomalous sound detection is to first learn an embedding space by using auxiliary classification tasks based on meta information or self-supervised learning and then estimate the distribution of normal data. In this work, AdaProj a novel loss function for training the embedding model is presented. In contrast to commonly used angular margin losses, which project data of each class as close as possible to their corresponding class centers, AdaProj learns to project data onto class-specific subspaces while still ensuring an angular margin between classes. By doing so, the resulting distributions of the embeddings belonging to normal data are not required to be as restrictive as other loss functions allowing a more detailed view on the data. In experiments conducted on the DCASE2022 and DCASE2023 anomalous sound detection datasets, it is shown that using AdaProj to learn an embedding space significantly outperforms other commonly used loss functions.
Vibration Sensitivity of one-port and two-port MEMS microphones
Authors: Francis Doyon-D'Amour, Carly Stalder, Timothy Hodges, Michel Stephan, Lixiue Wu, Triantafillos Koukoulas, Stephane Leahy, Raphael St-Gelais
Date: 2024-03-20 00:00:00
Tasks: Sensitivity

Micro-electro-mechanical system (MEMS) microphones (mics) with two acoustic ports are currently receiving considerable interest, with the promise of achieving higher directional sensitivity compared to traditional one-port architectures. However, measuring pressure differences in two-port microphones typically commands sensing elements that are softer than in one-port mics, and are therefore presumably more prone to interference from external vibration. Here we derive a universal expression for microphone sensitivity to vibration and we experimentally demonstrate its validity for several emerging two-port microphone technologies. We also perform vibration measurements on a one-port mic, thus providing a one-stop direct comparison between one-port and two-port sensing approaches. We find that the acoustically-referred vibration sensitivity of two-port MEMS mics, in units of measured acoustic pressure per external acceleration (i.e., Pascals per g), does not depend on the sensing element stiffness nor on its natural frequency. We also show that this vibration sensitivity in two-port mics is inversely proportional to frequency as opposed to the frequency independent behavior observed in one-port mics. This is confirmed experimentally for several types of microphone packages.
KunquDB: An Attempt for Speaker Verification in the Chinese Opera Scenario
Authors: Huali Zhou, Yuke Lin, Dong Liu, Ming Li
Date: 2024-03-20 00:00:00
Tasks: Domain Adaptation, Speaker Verification

This work aims to promote Chinese opera research in both musical and speech domains, with a primary focus on overcoming the data limitations. We introduce KunquDB, a relatively large-scale, well-annotated audio-visual dataset comprising 339 speakers and 128 hours of content. Originating from the Kunqu Opera Art Canon (Kunqu yishu dadian), KunquDB is meticulously structured by dialogue lines, providing explicit annotations including character names, speaker names, gender information, vocal manner classifications, and accompanied by preliminary text transcriptions. KunquDB provides a versatile foundation for role-centric acoustic studies and advancements in speech-related research, including Automatic Speaker Verification (ASV). Beyond enriching opera research, this dataset bridges the gap between artistic expression and technological innovation. Pioneering the exploration of ASV in Chinese opera, we construct four test trials considering two distinct vocal manners in opera voices: stage speech (ST) and singing (S). Implementing domain adaptation methods effectively mitigates domain mismatches induced by these vocal manner variations while there is still room for further improvement as a benchmark.
TDT-KWS: Fast And Accurate Keyword Spotting Using Token-and-duration Transducer
Authors: Yu Xi, Hao Li, Baochen Yang, Haoyu Li, Hainan Xu, Kai Yu
Date: 2024-03-20 00:00:00
Tasks: Keyword Spotting

Designing an efficient keyword spotting (KWS) system that delivers exceptional performance on resource-constrained edge devices has long been a subject of significant attention. Existing KWS search algorithms typically follow a frame-synchronous approach, where search decisions are made repeatedly at each frame despite the fact that most frames are keyword-irrelevant. In this paper, we propose TDT-KWS, which leverages token-and-duration Transducers (TDT) for KWS tasks. We also propose a novel KWS task-specific decoding algorithm for Transducer-based models, which supports highly effective frame-asynchronous keyword search in streaming speech scenarios. With evaluations conducted on both the public Hey Snips and self-constructed LibriKWS-20 datasets, our proposed KWS-decoding algorithm produces more accurate results than conventional ASR decoding algorithms. Additionally, TDT-KWS achieves on-par or better wake word detection performance than both RNN-T and traditional TDT-ASR systems while achieving significant inference speed-up. Furthermore, experiments show that TDT-KWS is more robust to noisy environments compared to RNN-T KWS.
Whose Side Are You On? Investigating the Political Stance of Large Language Models
Authors: Pagnarasmey Pit, Xingjun Ma, Mike Conway, Qingyu Chen, James Bailey, Henry Pit, Putrasmey Keo, Watey Diep, Yu-Gang Jiang
Date: 2024-03-15 00:00:00
Tasks: Fairness, Information Retrieval, Text Generation

Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval. As the widespread adoption of LLMs continues to surge, it becomes increasingly crucial to ensure that these models yield responses that are politically impartial, with the aim of preventing information bubbles, upholding fairness in representation, and mitigating confirmation bias. In this paper, we propose a quantitative framework and pipeline designed to systematically investigate the political orientation of LLMs. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. Across topics, the results indicate that LLMs exhibit a tendency to provide responses that closely align with liberal or left-leaning perspectives rather than conservative or right-leaning ones when user queries include details pertaining to occupation, race, or political affiliation. The findings presented in this study not only reaffirm earlier observations regarding the left-leaning characteristics of LLMs but also surface particular attributes, such as occupation, that are particularly susceptible to such inclinations even when directly steered towards conservatism. As a recommendation to avoid these models providing politicised responses, users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
Learning Hierarchical Control Systems for Autonomous Systems with Energy Constraints
Authors: Charlott Vallon, Mark Pustilnik, Alessandro Pinto, Francesco Borrelli
Date: 2024-03-21 00:00:00
Tasks: energy management, Management, Task Planning

This paper focuses on the design of hierarchical control architectures for autonomous systems with energy constraints. We focus on systems where energy storage limitations and slow recharge rates drastically affect the way the autonomous systems are operated. Using examples from space robotics and public transportation, we motivate the need for formally designed learning hierarchical control systems. We propose a learning control architecture which incorporates learning mechanisms at various levels of the control hierarchy to improve performance and resource utilization. The proposed hierarchical control scheme relies on high-level energy-aware task planning and assignment, complemented by a low-level predictive control mechanism responsible for the autonomous execution of tasks, including motion control and energy management. Simulation examples show the benefits and the limitations of the proposed architecture when learning is used to obtain a more energy-efficient task allocation.
Designing Robust Linear Output Feedback Controller based on CLF-CBF framework via Linear~Programming(LP-CLF-CBF)
Authors: Mahroo Bahreinian, Mehdi Kermanshah, Roberto Tron
Date: 2024-03-21 00:00:00
Tasks: Navigate

We consider the problem of designing output feedback controllers that use measurements from a set of landmarks to navigate through a cell-decomposable environment using duality, Control Lyapunov and Barrier Functions (CLF, CBF), and Linear Programming. We propose two objectives for navigating in an environment, one to traverse the environment by making loops and one by converging to a stabilization point while smoothing the transition between consecutive cells. We test our algorithms in a simulation environment, evaluating the robustness of the approach to practical conditions, such as bearing-only measurements, and measurements acquired with a camera with a limited field of view.
Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study
Authors: Riccardo Busetto, Valentina Breschi, Federica Baracchi, Simone Formentin
Date: 2024-03-21 00:00:00
Tasks: Meta-Learning

Data-driven control offers a viable option for control scenarios where constructing a system model is expensive or time-consuming. Nonetheless, many of these algorithms are not entirely automated, often necessitating the adjustment of multiple hyperparameters through cumbersome trial-and-error processes and demanding significant amounts of data. In this paper, we explore a meta-learning approach to leverage potentially existing prior knowledge about analogous (though not identical) systems, aiming to reduce both the experimental workload and ease the tuning of the available degrees of freedom. We validate this methodology through an experimental case study involving the tuning of proportional, integral (PI) controllers for brushless DC (BLDC) motors with variable loads and architectures.
Synthesizing Controller for Safe Navigation using Control Density Function
Authors: Joseph Moyalan, Sriram S. K. S Narayanan, Andrew Zheng, Umesh Vaidya
Date: 2024-03-21 00:00:00

We consider the problem of navigating a nonlinear dynamical system from some initial set to some target set while avoiding collision with an unsafe set. We extend the concept of density function to control density function (CDF) for solving navigation problems with safety constraints. The occupancy-based interpretation of the measure associated with the density function is instrumental in imposing the safety constraints. The navigation problem with safety constraints is formulated as a quadratic program (QP) using CDF. The existing approach using the control barrier function (CBF) also formulates the navigation problem with safety constraints as QP. One of the main advantages of the proposed QP using CDF compared to QP formulated using CBF is that both the convergence/stability and safety can be combined and imposed using the CDF. Simulation results involving the Duffing oscillator and safe navigation of Dubin car models are provided to verify the main findings of the paper.
Exploiting Over-The-Air Consensus for Collision Avoidance and Formation Control in Multi-Agent Systems
Authors: Michael Epp, Fabio Molinari, Joerg Raisch
Date: 2024-03-21 00:00:00
Tasks: Collision Avoidance

This paper introduces a distributed control method for multi-agent robotic systems employing Over the Air Consensus (OtA-Consensus). Designed for agents with decoupled single-integrator dynamics, this approach aims at efficient formation achievement and collision avoidance. As a distinctive feature, it leverages OtA's ability to exploit interference in wireless channels, a property traditionally considered a drawback, thus enhancing communication efficiency among robots. An analytical proof of asymptotic convergence is established for systems with time-varying communication topologies represented by sequences of strongly connected directed graphs. Comparative evaluations demonstrate significant efficiency improvements over current state-of-the-art methods, especially in scenarios with a large number of agents.
A new control-oriented METANET model to encompass service stations on highways
Authors: Ayda Kamalifar, Carlo Cenedese, Michele Cucuzzella, Antonella Ferrara
Date: 2024-03-21 00:00:00

In this paper, we propose the METANET with service station (METANET-s) model, a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. Specifically, we employ the (so-called) store-and-forward links to model the stop of vehicles and the possible queue forming in the process of merging back into the highway mainstream. We explore the capability of the METANET-s to capture well both traffic back propagation and capacity drops, which are typically caused by the presence of vehicles joining again the mainstream traffic from the service station. Therefore, capturing these effects is crucial to improving the model's predictive capabilities. Finally, we perform a comparative analysis with the Cell Transmission Model with service station (CTM-s), showcasing that the METANET-s describes the traffic evolution much better than its first-order counterpart.
A Benchmark for the Application of Distributed Control Techniques to the Electricity Network of the European Economic Area
Authors: A. Riccardi, L. Laurenti, B. De Schutter
Date: 2024-03-21 00:00:00
Tasks: Model Predictive Control

The European Economic Area Electricity Network Benchmark (EEA-ENB) is a multi-area power system representing the European network of transmission systems for electricity to facilitate the application of distributed control techniques. In the EEA-ENB we consider the Load Frequency Control (LFC) problem in the presence of renewable energy sources (RESs), and energy storage systems (ESSs). RESs are known to cause instability in power networks due to their inertia-less and intermittent characteristics, while ESSs are introduced as a resource to mitigate the problem. In the EEA-ENB, particular attention is dedicated to Distributed Model Predictive Control (DMPC), whose application is often limited to small and homogeneous test cases due to the lack of standardized large-scale scenarios for testing, and due to the large computation time required to obtain a centralized MPC action for performance comparison with DMPC strategies under consideration. The second problem is exacerbated when the scale of the system grows. To address these challenges and to provide a real-world-based and control-independent benchmark, the EEA-ENB has been developed. The benchmark includes a centralized MPC strategy providing performance and computation time metrics to compare distributed control within a repeatable and realistic simulation environment.
A Control Barrier Function Composition Approach for Multi-Agent Systems in Marine Applications
Authors: Yujia Yang, Chris Manzie, Ye Pu
Date: 2024-03-21 00:00:00
Tasks: Collision Avoidance

The agents within a multi-agent system (MAS) operating in marine environments often need to utilize task payloads and avoid collisions in coordination, necessitating adherence to a set of relative-pose constraints, which may include field-of-view, line-of-sight, collision-avoidance, and range constraints. A nominal controller designed for reference tracking may not guarantee the marine MAS stays safe w.r.t. these constraints. To modify the nominal input as one that enforces safety, we introduce a framework to systematically encode the relative-pose constraints as nonsmooth control barrier functions (NCBFs) and combine them as a single NCBF using Boolean composition, which enables a simplified verification process compared to using the NCBFs individually. While other relative-pose constraint functions have explicit derivatives, the challenging line-of-sight constraint is encoded with the minimum distance function between the line-of-sight set and other agents, whose derivative is not explicit. Hence, existing safe control design methods that consider composite NCBFs cannot be applied. To address this challenge, we propose a novel quadratic program formulation based on the dual of the minimum distance problem and develop a new theory to ensure the resulting control input guarantees constraint satisfaction. Lastly, we validate the effectiveness of our proposed framework on a simulated large-scale marine MAS and a real-world marine MAS comprising one Unmanned Surface Vehicle and two Unmanned Underwater Vehicles.
Transformation-Free Fixed-Structure Model Reduction for LPV Systems
Authors: Lennart Heeren, Adwait Datar, Antonio Mendez Gonzalez, Herbert Werner
Date: 2024-03-21 00:00:00

In this paper, we propose a model reduction technique for linear parameter varying (LPV) systems based on available tools for fixed-structure controller synthesis. We start by transforming a model reduction problem into an equivalent controller synthesis problem by defining an appropriate generalized plant. The controller synthesis problem is then solved by using gradient-based tools available in the literature. Owing to the flexibility of the gradient-based synthesis tools, we are able to impose a desired structure on the obtained reduced model. Additionally, we obtain a bound on the approximation error as a direct output of the optimization problem. The proposed methods are applied on a benchmark mechanical system of interconnected masses, springs and dampers. To evaluate the effect of the proposed model-reduction approach on controller design, LPV controllers designed using the reduced models (with and without an imposed structure) are compared in closed-loop with the original model.
Event-triggered Boundary Control of Mixed-autonomy Traffic
Authors: Yihuai Zhang, Huan Yu
Date: 2024-03-21 00:00:00
Tasks: Autonomous Vehicles, Friction

Control problems of mixed-autonomy traffic systems that consist of both human-driven vehicles (HV) and autonomous vehicles (AV), have gained increasing attention. This paper focuses on suppressing traffic oscillations in the mixed-autonomy traffic system using boundary control design. The mixed traffic dynamics are described by 4 x 4 hyperbolic partial differential equations (PDEs), governing the propagation of four waves of traffic, including the density of HV, the density of AV, the friction between the two vehicle classes from driving interactions and the averaged velocity. We propose an event-triggered boundary control design since control signals of the traffic light on ramp or the varying speed limit cannot be continuously updated. We apply the event-triggered mechanism for a PDE backstepping controller and obtain a dynamic triggering condition. Lyapunov analysis is performed to prove the exponential stability of the closed-loop system with the event-triggered controller. Numerical simulation demonstrates the efficiency of the proposed event-trigger control design. We analyzed how the car-following spacing of AV affects the event-triggering mechanism of the control input in mixed-autonomy traffic.
Conservative Linear Envelopes for Nonlinear, High-Dimensional, Hamilton-Jacobi Reachability
Authors: Will Sharpless, Yat Tin Chow, Sylvia Herbert
Date: 2024-03-21 00:00:00

Hamilton-Jacobi reachability (HJR) provides a value function that encodes the set of states from which a system with bounded control inputs can reach or avoid a target despite any bounded disturbance, and the corresponding robust, optimal control policy. Though powerful, traditional methods for HJR rely on dynamic programming (DP) and suffer from exponential computation growth with respect to state dimension. The recently favored Hopf formula mitigates this ``curse of dimensionality'' by providing an efficient and space-parallelizable approach for solving the reachability problem. However, the Hopf formula can only be applied to linear time-varying systems. To overcome this limitation, we show that the error between a nonlinear system and a linear model can be transformed into an adversarial bounded artificial disturbance. One may then solve the dimension-robust generalized Hopf formula for a linear game with this ``antagonistic error" to perform guaranteed conservative reachability analysis and control synthesis of nonlinear systems; this can be done for problem formulations in which no other HJR method is both computationally feasible and guaranteed. In addition, we offer several technical methods for reducing conservativeness in the analysis. We demonstrate the effectiveness of our results through one illustrative example (the controlled Van der Pol system) that can be compared to standard DP, and one higher-dimensional 15D example (a 5-agent pursuit-evasion game with Dubins cars).
Lane level joint control of off-ramp and main line speed guidance on expressway in rainy weather
Authors: Boyao Peng, Lexing Zhang, Enkai Li
Date: 2024-03-21 00:00:00
Tasks: Friction

In the upstream of the exit ramp of the expressway, the speed limit difference leads to a significant deceleration of the vehicle in the area adjacent to the off-ramp. The friction coefficient of the road surface decreases under rainy weather, and the above deceleration process can easily lead to sideslip and rollover of the vehicle. Dynamic speed guidance is an effective way to improve the status quo. Currently, there is an emerging trend to utilize I2V technology and high-precision map technology for lane level speed guidance control. This paper presents an optimized joint control strategy for main line-off-ramp speed guidance, which can adjust the guidance speed in real time according to the rainfall intensity. At the same time, this paper designs a progressive deceleration strategy, which works together with the speed guidance control to ensure the safe deceleration of vehicles. The simulation results show that the proposed control strategy outperforms the fixed speed limit control in terms of improving the total traveled time (TTT), total traveled distance (TTD) and standard deviation of speed (SD). Sensitivity analysis shows that the proposed control strategy can improve performance with the increase of the compliance rate of drivers. The speed guidance control method established in this paper can improve the vehicle operation efficiency in the off-ramp area of the expressway and reduce the speed difference of each vehicle in rainy weather, which guarantee the safety of expressway driving in the rainy day.