pwc-archive/papers-with-abstracts huggingface dataset:
WebSuite: Systematically Evaluating Why Web Agents Fail
Authors: Eric Li, Jim Waldo
Date: 2024-06-01 00:00:00
Tasks: Benchmarking, Diagnostic

We describe WebSuite, the first diagnostic benchmark for generalist web agents, designed to systematically evaluate why agents fail. Advances in AI have led to the rise of numerous web agents that autonomously operate a browser to complete tasks. However, most existing benchmarks focus on strictly measuring whether an agent can or cannot complete a task, without giving insight on why. In this paper, we 1) develop a taxonomy of web actions to facilitate identifying common failure patterns, and 2) create an extensible benchmark suite to assess agents' performance on our taxonomized actions. This benchmark suite consists of both individual tasks, such as clicking a button, and end-to-end tasks, such as adding an item to a cart, and is designed such that any failure of a task can be attributed directly to a failure of a specific web action. We evaluate two popular generalist web agents, one text-based and one multimodal, and identify unique weaknesses for each agent. Because WebSuite can disaggregate task failures into specific action failures, this enables granular identification of which UX flows an individual agent has trouble with and immediately highlights promising avenues for improvement. These findings highlight the need for more focused benchmarking on where web agents go wrong to effectively improve agents beyond their weaker performance today.
LinkLogic: A New Method and Benchmark for Explainable Knowledge Graph Predictions
Authors: Niraj Kumar-Singh, Gustavo Polleti, Saee Paliwal, Rachel Hodos-Nkhereanye
Date: 2024-06-02 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Prediction

While there are a plethora of methods for link prediction in knowledge graphs, state-of-the-art approaches are often black box, obfuscating model reasoning and thereby limiting the ability of users to make informed decisions about model predictions. Recently, methods have emerged to generate prediction explanations for Knowledge Graph Embedding models, a widely-used class of methods for link prediction. The question then becomes, how well do these explanation systems work? To date this has generally been addressed anecdotally, or through time-consuming user research. In this work, we present an in-depth exploration of a simple link prediction explanation method we call LinkLogic, that surfaces and ranks explanatory information used for the prediction. Importantly, we construct the first-ever link prediction explanation benchmark, based on family structures present in the FB13 dataset. We demonstrate the use of this benchmark as a rich evaluation sandbox, probing LinkLogic quantitatively and qualitatively to assess the fidelity, selectivity and relevance of the generated explanations. We hope our work paves the way for more holistic and empirical assessment of knowledge graph prediction explanation methods in the future.
Exponentially Stable Projector-based Control of Lagrangian Systems with Gaussian Processes
Authors: Giulio Evangelisti, Cosimo Della Santina, Sandra Hirche
Date: 2024-06-05 00:00:00
Tasks: Gaussian Processes, Uncertainty Quantification

Designing accurate yet robust tracking controllers with tight performance guarantees for Lagrangian systems is challenging due to nonlinear modeling uncertainties and conservative stability criteria. This article proposes a structure-preserving projector-based tracking control law for uncertain Euler-Lagrange (EL) systems using physically consistent Lagrangian-Gaussian Processes (L-GPs). We leverage the uncertainty quantification of the L-GP for adaptive feedforward-feedback balancing. In particular, an accurate probabilistic guarantee for exponential stability is derived by leveraging matrix analysis results and contraction theory, where the benefit of the proposed controller is proven and shown in the closed-form expressions for convergence rate and radius. Extensive numerical simulations not only demonstrate the controller's efficacy based on a two-link and a soft robotic manipulator but also all theoretical results are explicitly analyzed and validated.
Study on layout of double rotated serpentine springs for vertical-comb-driven torsional micromirror
Authors: Biyun Ling, Yuhu Xia, Minli Cai, Xiaoyue Wang, Yaming Wu
Date: 2024-06-05 00:00:00

The combination of double rotated serpentine springs (RSS) and vertical comb-drive is a suitbale solution for the development of torsional micromirror with high fill factor, low fabrication difficulty and good performance. However, the alignment error between upper and lower comb set caused by fabrication can induce force with unexpected direction. And the cross-axis coupled spring constants in double rotated serpentine springs (DRSSs) makes micromirror more susceptible to this alignment error. Herein, in order to minimize the unexpected deflection caused by alignment error of vertical-comb-driven micromirror, this paper, for the first time, studies the effect of layout (centrosymmetrically-arranged and axisymmetrically-arranged) of DRSSs on cross-axis coupled spring constants. Both of theoretical analysis and finite element analysis (FEA) simulation are conducted to reveal this phenomenon. With an example, centrosymmetrically-arranged DRSSs are proved to be more resistant to pull-in of two comb sets. Finally, the relationship between key structure parameters and cross-axis coupled spring constants of centrosymmetrically -arranged DRSSs are presented.
Real-Time Small-Signal Security Assessment Using Graph Neural Networks
Authors: Glory Justin, Santiago Paternain
Date: 2024-06-05 00:00:00
Tasks: Graph Neural Network

Security assessment is one of the most crucial functions of a power system operator. However, growing complexity and unpredictability make this an increasingly complex and computationally difficult task. In recent times, machine learning methods have gained attention for their ability to handle complex modeling applications. Some methods proposed include deep learning using convolutional neural networks, decision trees, etc. While these methods generate promising results, most methods still require long training times and computational resources. This paper proposes a graph neural network (GNN) approach to the small-signal security assessment problem using data from Phasor Measurement Units (PMUs). Leveraging the inherently graphical structure of the power grid using GNNs, training times can be reduced and efficiency improved for real-time application. Also, using graph properties, optimal PMU placement is determined and the proposed method is shown to perform efficiently under partial observability with limited PMU data. Case studies with simulated data from the IEEE 68-bus system and the NPCC 140-bus system are used to verify the effectiveness of the proposed method.
Development of an underwater inductive coupling communication system with power carrier technology
Authors: Zhongxing Zhang
Date: 2024-06-05 00:00:00

Inductive coupling communication is one of the main methods of underwater communication systems due to its excellent comprehensive performance. However, the data transmission distance and operational power consumption need to be further enhanced. In this paper, an underwater induction coupling communication scheme based on power carrier technology is proposed to improve the transmission speed and reduce the bit error rate. The microcontroller of STM32L series with ultra-low power consumption was employed as the core of the system. Through the construction and simulation of the communication channel, the optimal parameters were determined. According to the circuit model of the power carrier communication, the effect of different modulation and demodulation methods to the signal transmission quality were discussed, which demonstrates the superiority of Differential Phase Shift Keying (DPSK). With the system-level low power design and onboard communication quality optimization, the device was developed. The test results in the laboratory environment show that the system can achieve efficient data communication with a rate of 115200bps and static power consumption as low as 660{\mu}A in the 700m channel. This study provides a practical design approach for the high-speed communication and Low-power operation of underwater communication systems.
Multimodal Resonance in Strongly Coupled Inductor Arrays
Authors: Robert R. Hughes, James Treisman, Alexis Hernandez Arroyo, Anthony J. Mulholland
Date: 2024-06-04 00:00:00

Magnetic resonance coupling (MRC) is widely used for wireless power transfer (WPT) applications, but little work has explored how MRC phenomena could be exploited for sensing applications. This paper introduces, validates and evaluates the unique multi-resonant phenomena predicted by circuit theory for over-coupled inductive arrays, and presents eigen-formulae for calculating resonant frequencies and voltage modes within passively excited arrays. Finite-element simulations and experimental results demonstrate the validity of the multi-modal resonant principles for strongly-coupled inductor arrays. The results confirm the distinctive multi-modal resonant frequencies these arrays exhibit, corresponding to the specific magnetic excitation "modes" (comparable to vibrational modes in multi-degree-of-freedom systems). The theoretical and finite element models presented offer a framework for designing and optimizing novel inductive sensing arrays, capitalizing on the unique resonant effects of over-coupling and exploiting their potential magnetic field shaping.
Computation-Aware Learning for Stable Control with Gaussian Process
Authors: Wenhan Cao, Alexandre Capone, Rishabh Yadav, Sandra Hirche, Wei Pan
Date: 2024-06-04 00:00:00

In Gaussian Process (GP) dynamical model learning for robot control, particularly for systems constrained by computational resources like small quadrotors equipped with low-end processors, analyzing stability and designing a stable controller present significant challenges. This paper distinguishes between two types of uncertainty within the posteriors of GP dynamical models: the well-documented mathematical uncertainty stemming from limited data and computational uncertainty arising from constrained computational capabilities, which has been largely overlooked in prior research. Our work demonstrates that computational uncertainty, quantified through a probabilistic approximation of the inverse covariance matrix in GP dynamical models, is essential for stable control under computational constraints. We show that incorporating computational uncertainty can prevent overestimating the region of attraction, a safe subset of the state space with asymptotic stability, thus improving system safety. Building on these insights, we propose an innovative controller design methodology that integrates computational uncertainty within a second-order cone programming framework. Simulations of canonical stable control tasks and experiments of quadrotor tracking exhibit the effectiveness of our method under computational constraints.
Novel pre-emptive control solutions for V2X connected electric vehicles
Authors: Kai Man So, Gaetano Tavolo, Davide Tavernini, Marco Grosso, Sergio Pozzato, Pietro Perlo, Aldo Sorniotti
Date: 2024-06-04 00:00:00
Tasks: Friction, Position

V2X technologies will become widespread in the next generation of passenger cars, and enable the development of novel vehicle control functionalities. Although a wide literature describes the energy efficiency benefits of V2X connectivity, e.g., in terms of vehicle speed profiling and platooning, there is a gap in the analysis of the potential of vehicle connectivity in enhancing the performance of active safety control systems. To highlight the impact vehicle connectivity could have on future active safety systems, this paper presents two novel control functions for connected vehicles, benefitting from the precise knowledge of the expected path and tire-road friction conditions ahead, as well as the current position of the ego vehicle. These functions, developed within recent and ongoing European projects, are: i) pre-emptive traction control; and ii) pre-emptive braking control.
Nonlinear Model Predictive Control for Preview-Based Traction Control
Authors: Gaetano Tavolo, Kai Man So, Davide Tavernini, Pietro Perlo, Aldo Sorniotti
Date: 2024-06-04 00:00:00
Tasks: Friction, Model Predictive Control, Sensitivity

This study presents a nonlinear model predictive control (NMPC) formulation for preview-based traction control, which uses the information on the expected tire-road friction coefficient ahead to enhance the wheel slip control performance, in the context of connected vehicles with V2X features. Proof-of-concept experiments on an electric vehicle prototype highlight the real-time capability of the controller, and the wheel slip control performance improvement brought by the tire-road friction coefficient preview. Finally, an experimentally validated simulation model is used in sensitivity analyses, to evaluate the performance benefit of the preview-based controller for different dynamic characteristics (e.g., time constant and pure time delays) of the electric powertrains.
Nonlinear Model Predictive Control for Enhanced Path Tracking and Autonomous Drifting through Direct Yaw Moment Control and Rear-Wheel-Steering
Authors: Gaetano Tavolo, Pietro Stano, Davide Tavernini, Umberto Montanaro, Manuela Tufo, Giovanni Fiengo, Pietro Perlo, Aldo Sorniotti
Date: 2024-06-04 00:00:00
Tasks: Model Predictive Control

Path tracking (PT) controllers capable of replicating race driving techniques, such as drifting beyond the limits of handling, have the potential of enhancing active safety in critical conditions. This paper presents a nonlinear model predictive control (NMPC) approach that integrates multiple actuation methods, namely four-wheel-steering, longitudinal tyre force distribution, and direct yaw moment control, to execute drifting when this is beneficial for PT in emergency scenarios. Simulation results of challenging manoeuvres, based on an experimentally validated vehicle model, highlight the substantial PT performance improvements brought by: i) vehicle operation outside the envelope enforced by the current generation of stability controllers; and ii) the integrated control of multiple actuators.
Age of Trust (AoT): A Continuous Verification Framework for Wireless Networks
Authors: Yuquan Xiao, Qinghe Du, Wenchi Cheng, Panagiotis D. Diamantoulakis, George K. Karagiannidis
Date: 2024-06-04 00:00:00
Tasks: Philosophy, Q-Learning

Zero Trust is a new security vision for 6G networks that emphasises the philosophy of never trust and always verify. However, there is a fundamental trade-off between the wireless transmission efficiency and the trust level, which is reflected by the verification interval and its adaptation strategy. More importantly, the mathematical framework to characterise the trust level of the adaptive verification strategy is still missing. Inspired by this vision, we propose a concept called age of trust (AoT) to capture the characteristics of the trust level degrading over time, with the definition of the time elapsed since the last verification of the target user's trust plus the initial age, which depends on the trust level evaluated at that verification. The higher the trust level, the lower the initial age. To evaluate the trust level in the long term, the average AoT is used. We then investigate how to find a compromise between average AoT and wireless transmission efficiency with limited resources. In particular, we address the bi-objective optimization (BOO) problem between average AoT and throughput over a single link with arbitrary service process, where the identity of the receiver is constantly verified, and we devise a periodic verification scheme and a Q-learning-based scheme for constant process and random process, respectively. We also tackle the BOO problem in a multiple random access scenario, where a trust-enhanced frame-slotted ALOHA is designed. Finally, the numerical results show that our proposals can achieve a fair compromise between trust level and wireless transmission efficiency, and thus have a wide application prospect in various zero-trust architectures.
Statistical Age of Information: A Risk-Aware Metric and Its Applications in Status Updates
Authors: Yuquan Xiao, Qinghe Du, George K. Karagiannidis
Date: 2024-06-04 00:00:00

Age of information (AoI) is an effective measure to quantify the information freshness in wireless status update systems. It has been further validated that the peak AoI has the potential to capture the core characteristics of the aging process, and thus the average peak AoI is widely used to evaluate the long-term performance of information freshness. However, the average peak AoI is a risk-insensitive metric and therefore may not be well suited for evaluating critical status update services. Motivated by this concern, and following the spirit of entropic value-at-risk (EVaR) in the field of risk analysis, in this paper we present a concept, termed Statistical AoI, for providing a unified framework to guarantee various requirements of risk-sensitive status-update services with the demand on the violation probability of the peak age. In particular, as the constraint on the violation probability of the peak age varies from loose to strict, the statistical AoI evolves from the average peak AoI to the maximum peak AoI. We then investigate the statistical AoI minimization problem for status updates over wireless fading channels. It is interesting to note that the corresponding optimal sampling scheme varies from step to constant functions of the channel power gain with the peak age violation probability from one to zero. We also address the maximum statistical AoI minimization problem for multi-status updates with time division multiple access (TDMA), where longer transmission time can improve reliability but may also cause the larger age. By solving this problem, we derive the optimal transmission time allocation scheme. Numerical results show that our proposals can better satisfy the diverse requirements of various risk-sensitive status update services, and demonstrate the great potential of improving information freshness compared to baseline approaches.
Input-to-state stability of infinite-dimensional systems: Foundations and present-day developments
Authors: Andrii Mironchenko, Christophe Prieur
Date: 2024-06-04 00:00:00

Input-to-state stability (ISS) unifies the stability and robustness in one notion, and serves as a basis for broad areas of nonlinear control theory. In this contribution, we covered the most fundamental facts in the infinite-dimensional ISS theory with a stress on Lyapunov methods. We consider various applications given by different classes of infinite-dimensional systems. Finally, we discuss a Lyapunov-based small-gain theorem for stability analysis of an interconnection of two ISS systems.
Stochastic Carbon Footprint Tracing Methods in Power Systems
Authors: Jiashuo Hu, Xiao-Ping Zhang, Youwei Jia
Date: 2024-06-04 00:00:00
Tasks: Computational Efficiency

As the penetration of distributed energy resources (DER) and renewable energy sources (RES) increases, carbon footprint tracking requires more granular analysis results. Existing carbon footprint tracking methods focus on deterministic steady-state analysis where the high uncertainties of RES cannot be considered. Considering the deficiency of the existing deterministic method, this paper proposes two stochastic carbon footprint tracking methods to cope with the impact of RES uncertainty on load-side carbon footprint tracing. The first method introduces probabilistic analysis in the framework of carbon emissions flow (CEF) to provide a global reference for the spatial characteristic of the power system component carbon intensity distribution. Considering that the CEF network expands with the increasing penetration of DERs, the second method can effectively improve the computational efficiency over the first method while ensuring the computational accuracy on the large power systems. These proposed models are tested and compared in a synthetic 1004-bus test system in the case study to demonstrate the performance of the two proposed methods
Electric Vehicle Adoption Modeling in France: A Systematic Literature Review
Authors: Karsi Widiawati, Bertha M. Sopha, Naly Rakoto
Date: 2024-06-04 00:00:00
Tasks: Systematic Literature Review

France is one of the pioneer countries in the use of Electric Vehicles (EVs). The French government aims to complete the transition to EVs by 2040. Therefore, modeling related to the adoption of EVs is needed in order to determine the potential policies needed to achieve this goal. This modeling is based on a literature study to identify the factors and the causal relationship between those factors. The systematic literature review (SLR) analysis was performed on 20 journals selected based on PRISMA filtering. From this SLR analysis, five direct factors and four indirect factors were obtained which were then used as the basis for modeling. Based on the model developed, four balancing (B) loops and three reinforcing (R) loops were obtained. From the analysis, it was found that the advertising factor has a goal seeking structure, while the word of mouth, environmentally friendly image, and total cost of ownership factors have an S-shaped structure.
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis
Authors: Niladri Das, Ross Guttromson, Tommie A. Catanach
Date: 2024-06-04 00:00:00
Tasks: Bayesian Inference

Combined electric power system and High-Altitude Electromagnetic Pulse (HEMP) models are being developed to determine the effect of a HEMP on the US power grid. The work relies primarily on deterministic methods; however, it is computationally untenable to evaluate the E1 HEMP response of large numbers of grid components distributed across a large interconnection. Further, the deterministic assessment of these components' failures are largely unachievable. E1 HEMP laboratory testing of the components is accomplished, but is expensive, leaving few data points to construct failure models of grid components exposed to E1 HEMP. The use of Bayesian priors, developed using the subject matter expertise, combined with the minimal test data in a Bayesian inference process, provides the basis for the development of more robust and cost-effective statistical component failure models. These can be used with minimal computational burden in a simulation environment such as sampling of Cumulative Distribution Functions (CDFs).
Windex: Realtime Neural Whittle Indexing for Scalable Service Guarantees in NextG Cellular Networks
Authors: Archana Bura, Ushasi Ghosh, Dinesh Bharadia, Srinivas Shakkottai
Date: 2024-06-04 00:00:00
Tasks: Scheduling

We address the resource allocation challenges in NextG cellular radio access networks (RAN), where heterogeneous user applications demand guarantees on throughput and service regularity. We leverage the Whittle indexability property to decompose the resource allocation problem, enabling the independent computation of relative priorities for each user. By simply allocating resources in decreasing order of these indices, we transform the combinatorial complexity of resource allocation into a linear one. We propose Windex, a lightweight approach for training neural networks to compute Whittle indices, considering constraint violation, channel quality, and system load. Implemented on a real-time RAN intelligent controller (RIC), our approach enables resource allocation decision times of less than 20$\mu$s per user and efficiently allocates resources in each 1ms scheduling time slot. Evaluation across standardized 3GPP service classes demonstrates significant improvements in service guarantees compared to existing schedulers, validated through simulations and emulations with over-the-air channel traces on a 5G testbed.