pwc-archive/papers-with-abstracts huggingface dataset:
Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide
Authors: Zhijie Duan, Kai Wei, Zhaoqian Xue, Jiayan Zhou, Shu Yang, Siyuan Ma, Jin Jin, Lingyao Li
Date: 2025-04-06 00:00:00
Tasks: graph construction, Pharmacovigilance

Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data from unstructured and noisy social media content remains a challenging task. We present a systematic framework that leverages large language models (LLMs) to extract medication side effects from social media and organize them into a knowledge graph (KG). We apply this framework to semaglutide for weight loss using data from Reddit. Using the constructed knowledge graph, we perform comprehensive analyses to investigate reported side effects across different semaglutide brands over time. These findings are further validated through comparison with adverse events reported in the FAERS database, providing important patient-centered insights into semaglutide's side effects that complement its safety profile and current knowledge base of semaglutide for both healthcare professionals and patients. Our work demonstrates the feasibility of using LLMs to transform social media data into structured KGs for pharmacovigilance.
Graph-based Diffusion Model for Collaborative Filtering
Authors: Xuan Zhang, Xiang Deng, Hongxing Yuan, Chunyu Wei, Yushun Fan
Date: 2025-04-07 00:00:00
Tasks: Collaborative Filtering, model

Recently, diffusion-based recommendation methods have achieved impressive results. However, existing approaches predominantly treat each user's historical interactions as independent training samples, overlooking the potential of higher-order collaborative signals between users and items. Such signals, which encapsulate richer and more nuanced relationships, can be naturally captured using graph-based data structures. To address this limitation, we extend diffusion-based recommendation methods to the graph domain by directly modeling user-item bipartite graphs with diffusion models. This enables better modeling of the higher-order connectivity inherent in complex interaction dynamics. However, this extension introduces two primary challenges: (1) Noise Heterogeneity, where interactions are influenced by various forms of continuous and discrete noise, and (2) Relation Explosion, referring to the high computational costs of processing large-scale graphs. To tackle these challenges, we propose a Graph-based Diffusion Model for Collaborative Filtering (GDMCF). To address noise heterogeneity, we introduce a multi-level noise corruption mechanism that integrates both continuous and discrete noise, effectively simulating real-world interaction complexities. To mitigate relation explosion, we design a user-active guided diffusion process that selectively focuses on the most meaningful edges and active users, reducing inference costs while preserving the graph's topological integrity. Extensive experiments on three benchmark datasets demonstrate that GDMCF consistently outperforms state-of-the-art methods, highlighting its effectiveness in capturing higher-order collaborative signals and improving recommendation performance.
Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator
Authors: Bing Wang, Bingrui Zhao, Ximing Li, Changchun Li, Wanfu Gao, Shengsheng Wang
Date: 2025-04-05 00:00:00

Over the past decade, social media platforms have been key in spreading rumors, leading to significant negative impacts. To counter this, the community has developed various Rumor Detection (RD) algorithms to automatically identify them using user comments as evidence. However, these RD methods often fail in the early stages of rumor propagation when only limited user comments are available, leading the community to focus on a more challenging topic named Rumor Early Detection (RED). Typically, existing RED methods learn from limited semantics in early comments. However, our preliminary experiment reveals that the RED models always perform best when the number of training and test comments is consistent and extensive. This inspires us to address the RED issue by generating more human-like comments to support this hypothesis. To implement this idea, we tune a comment generator by simulating expert collaboration and controversy and propose a new RED framework named CAMERED. Specifically, we integrate a mixture-of-expert structure into a generative language model and present a novel routing network for expert collaboration. Additionally, we synthesize a knowledgeable dataset and design an adversarial learning strategy to align the style of generated comments with real-world comments. We further integrate generated and original comments with a mutual controversy fusion module. Experimental results show that CAMERED outperforms state-of-the-art RED baseline models and generation methods, demonstrating its effectiveness.
Episodically adapted network-based controllers
Authors: Sruti Mallik, ShiNung Ching
Date: 2025-04-10 00:00:00

We consider the problem of distributing a control policy across a network of interconnected units. Distributing controllers in this way has a number of potential advantages, especially in terms of robustness, as the failure of a single unit can be compensated by the activity of others. However, it is not obvious a priori how such network-based controllers should be constructed for any given system and control objective. Here, we propose a synthesis procedure for obtaining dynamical networks that enact well-defined control policies in a model-free manner. We specifically consider an augmented state space consisting of both the plant state and the network states. Solution of an optimization problem in this augmented state space produces a desired objective and specification of the network dynamics. Because of the analytical tractability of this method, we are able to provide convergence and robustness assessments
Generalized Passivity Sensitivity Methodology for Small-Signal Stability Analysis
Authors: Dongyeong Lee, Francisco Javier Cifuentes Garcia, Jef Beerten
Date: 2025-04-10 00:00:00
Tasks: Sensitivity

This paper proposes a generalized passivity sensitivity analysis for power system stability studies. The method uncovers the most effective instability mitigation actions for both device-level and system-level investigations. The particular structure of the admittance and nodal models is exploited in the detailed derivation of the passivity sensitivity expressions. These proposed sensitivities are validated for different parameters at device-level and at system-level. Compared to previous stability and sensitivity methods, it does not require detailed system information, such as exact system eigenvalues, while it provides valuable information for a less conservative stable system design. In addition, we demonstrate how to utilize the proposed method through case studies with different converter controls and system-wide insights showing its general applicability.
Hybrid Control Barrier Functions for Nonholonomic Multi-Agent Systems
Authors: Aurora Haraldsen, Josef Matous, Kristin Y. Pettersen
Date: 2025-04-07 00:00:00

This paper addresses the problem of guaranteeing safety of multiple coordinated agents moving in dynamic environments. It has recently been shown that this problem can be efficiently solved through the notion of Control Barrier Functions (CBFs). However, for nonholonomic vehicles that are required to keep positive speeds, existing CBFs lose their validity. To overcome this limitation, we propose a hybrid formulation based on synergistic CBFs (SCBFs), which leverages a discrete switching mechanism to avoid configurations that would render the CBF invalid. Unlike existing approaches, our method ensures safety in the presence of moving obstacles and inter-agent interactions while respecting nonzero speed restrictions. We formally analyze the feasibility of the constraints with respect to actuation limits, and the efficacy of the solution is demonstrated in simulation of a multi-agent coordination problem in the presence of moving obstacles.
On-Chip and Off-Chip TIA Amplifiers for Nanopore Signal Readout Design, Performance and Challenges: A Review
Authors: K. Ashoka Deepthi, Manoj Varma, Arup Polley
Date: 2025-04-10 00:00:00
Tasks: Blocking, Diagnostic

Advancements in biomedical research have driven continuous innovations in sensing and diagnostic technologies. Among these, nanopore based single molecule sensing and sequencing is rapidly emerging as a powerful and versatile sensing methodology. Advancements in nanopore based approaches require concomitant improvements in the electronic readout methods employed, from the point of low noise, bandwidth and form factor. This article focuses on current sensing circuits designed and employed for ultra low noise nanopore signal readout, addressing the fundamental limitations of traditional off chip transimpedance amplifiers (TIAs), which suffer from high input parasitic capacitance, bandwidth constraints, and increased noise at high frequencies. This review explores the latest design schemes and circuit structures classified into on-chip and off-chip TIA designs, highlighting their design implementation, performance, respective challenges and explores the interplay between noise performance, capacitance, and bandwidth across diverse transimpedance amplifier (TIA) configurations. Emphasis is placed on characterizing noise response under varying parasitic capacitance and operational frequencies, a systematic evaluation not extensively addressed in prior literature while also considering the allowable input current compliance range limitations. The review also compares the widely used Axopatch 200B system to the designs reported in literature. The findings offer valuable insights into optimizing TIA designs for enhanced signal integrity in high speed and high sensitivity applications focusing on noise reduction, impedance matching, DC blocking, and offset cancellation techniques.
Optimal Frequency Support from Virtual Power Plants: Minimal Reserve and Allocation
Authors: Xiang Zhu, Guangchun Ruan, Hua Geng
Date: 2025-04-10 00:00:00

This paper proposes a novel reserve-minimizing and allocation strategy for virtual power plants (VPPs) to deliver optimal frequency support. The proposed strategy enables VPPs, acting as aggregators for inverter-based resources (IBRs), to provide optimal frequency support economically. The proposed strategy captures time-varying active power injections, reducing the unnecessary redundancy compared to traditional fixed reserve schemes. Reserve requirements for the VPPs are determined based on system frequency response and safety constraints, ensuring efficient grid support. Furthermore, an energy-based allocation model decomposes power injections for each IBR, accounting for their specific limitations. Numerical experiments validate the feasibility of the proposed approach, highlighting significant financial gains for VPPs, especially as system inertia decreases due to higher renewable energy integration.
Distributed Fault-Tolerant Control for Heterogeneous MAS with Prescribed Performance under Communication Failures
Authors: Yongkang Zhang, Bin Jiang, Yajie Ma
Date: 2025-04-10 00:00:00

This paper presents a novel approach employing prescribed performance control to address the distributed fault-tolerant formation control problem in a heterogeneous UAV-UGV cooperative system under a directed interaction topology and communication link failures. The proposed distributed fault-tolerant control scheme enables UAVs to accurately track a virtual leader's trajectory and achieve the desired formation, while ensuring UGVs converge within the convex hull formed by leader UAVs. By accounting for differences in system parameters and state dimensions between UAVs and UGVs, the method leverages performance functions to guarantee predefined transient and steady-state behavior. Additionally, a variable prescribed performance boundary control strategy with an adaptive learning rate is introduced to tackle actuator saturation, ensuring reliable formation tracking in real-world scenarios. Simulation results demonstrate the effectiveness and robustness of the proposed approach.
Robustness of Online Identification-based Policy Iteration to Noisy Data
Authors: Bowen Song, Andrea Iannelli
Date: 2025-04-10 00:00:00

This article investigates the core mechanisms of indirect data-driven control for unknown systems, focusing on the application of policy iteration (PI) within the context of the linear quadratic regulator (LQR) optimal control problem. Specifically, we consider a setting where data is collected sequentially from a linear system subject to exogenous process noise, and is then used to refine estimates of the optimal control policy. We integrate recursive least squares (RLS) for online model estimation within a certainty-equivalent framework, and employ PI to iteratively update the control policy. In this work, we investigate first the convergence behavior of RLS under two different models of adversarial noise, namely point-wise and energy bounded noise, and then we provide a closed-loop analysis of the combined model identification and control design process. This iterative scheme is formulated as an algorithmic dynamical system consisting of the feedback interconnection between two algorithms expressed as discrete-time systems. This system theoretic viewpoint on indirect data-driven control allows us to establish convergence guarantees to the optimal controller in the face of uncertainty caused by noisy data. Simulations illustrate the theoretical results.
Controlling Complex Systems
Authors: Marco Coraggio, Davide Salzano, Mario di Bernardo
Date: 2025-04-10 00:00:00

This chapter provides a comprehensive overview of controlling collective behavior in complex systems comprising large ensembles of interacting dynamical agents. Building upon traditional control theory's foundation in individual systems, we introduce tools designed to address the unique challenges of coordinating networks that exhibit emergent phenomena, including consensus, synchronization, and pattern formation. We analyze how local agent interactions generate macroscopic behaviors and investigate the fundamental role of network topology in determining system dynamics. Inspired by natural systems, we emphasize control strategies that achieve global coordination through localized interventions while considering practical implementation challenges. The chapter concludes by presenting novel frameworks for managing very large agent ensembles and leveraging interacting networks for control purposes.
Topology optimization of decoupling feeding networks for antenna arrays
Authors: Pan Lu, Eddie Wadbro, Jonas Starck, Martin Berggren, Emadeldeen Hassan
Date: 2025-04-10 00:00:00

Near-field and radiation coupling between nearby radiating elements is unavoidable, and it is considered a limiting factor for applications in wireless communications and active sensing. This article proposes a density-based topology optimization approach to design decoupling networks for such systems. The decoupling networks are designed based on a multi-objective optimization problem with the radiating elements replaced by their time-domain impulse response for efficient computations and to enable the solution of the design problem using gradient-based optimization methods. We use the adjoint-field method to compute the gradients of the optimization objectives. Additionally, nonlinear filters are applied during the optimization procedure to impose minimum-size control on the optimized designs. We demonstrate the concept by designing the decoupling network for a two-element planar antenna array; the antenna is designed in a separate optimization problem. The optimized decoupling networks provide a signal path that destructively interferes with the coupling between the radiating elements while preserving their individual matching to the feeding ports. Compact decoupling networks capable of suppressing the mutual coupling by more than 10 dB between two closely separated planar antennas operating around 2.45 GHz are presented and validated experimentally.
Strategic learning for disturbance rejection in multi-agent systems: Nash and Minmax in graphical games
Authors: Xinyang Wang, Martin Guay, Shimin Wang, Hongwei Zhang
Date: 2025-04-10 00:00:00

This article investigates the optimal control problem with disturbance rejection for discrete-time multi-agent systems under cooperative and non-cooperative graphical games frameworks. Given the practical challenges of obtaining accurate models, Q-function-based policy iteration methods are proposed to seek the Nash equilibrium solution for the cooperative graphical game and the distributed minmax solution for the non-cooperative graphical game. To implement these methods online, two reinforcement learning frameworks are developed, an actor-disturber-critic structure for the cooperative graphical game and an actor-adversary-disturber-critic structure for the non-cooperative graphical game. The stability of the proposed methods is rigorously analyzed, and simulation results are provided to illustrate the effectiveness of the proposed methods.
Quickest change detection for UAV-based sensing
Authors: Saqib Abbas, Anurag Kumar, Arpan Chattopadhyay
Date: 2025-04-10 00:00:00
Tasks: Change Detection

This paper addresses the problem of quickest change detection (QCD) at two spatially separated locations monitored by a single unmanned aerial vehicle (UAV) equipped with a sensor. At any location, the UAV observes i.i.d. data sequentially in discrete time instants. The distribution of the observation data changes at some unknown, arbitrary time and the UAV has to detect this change in the shortest possible time. Change can occur at most at one location over the entire infinite time horizon. The UAV switches between these two locations in order to quickly detect the change. To this end, we propose Location Switching and Change Detection (LS-CD) algorithm which uses a repeated one-sided sequential probability ratio test (SPRT) based mechanism for observation-driven location switching and change detection. The primary goal is to minimize the worst-case average detection delay (WADD) while meeting constraints on the average run length to false alarm (ARL2FA) and the UAV's time-averaged energy consumption. We provide a rigorous theoretical analysis of the algorithm's performance by using theory of random walk. Specifically, we derive tight upper and lower bounds to its ARL2FA and a tight upper bound to its WADD. In the special case of a symmetrical setting, our analysis leads to a new asymptotic upper bound to the ARL2FA of the standard CUSUM algorithm, a novel contribution not available in the literature, to our knowledge. Numerical simulations demonstrate the efficacy of LS-CD.
Can Carbon-Aware Electric Load Shifting Reduce Emissions? An Equilibrium-Based Analysis
Authors: Wenqian Jiang, Olivier Huber, Michael C. Ferris, Line Roald
Date: 2025-04-09 00:00:00
Tasks: Benchmarking

An increasing number of electric loads, such as hydrogen producers or data centers, can be characterized as carbon-sensitive, meaning that they are willing to adapt the timing and/or location of their electricity usage in order to minimize carbon footprints. However, the emission reduction efforts of these carbon-sensitive loads rely on carbon intensity information such as average carbon emissions, and it is unclear whether load shifting based on these signals effectively reduces carbon emissions. To address this open question, we investigate the impact of carbon-sensitive consumers using equilibrium analysis. Specifically, we expand the commonly used equilibrium model for electricity market clearing to include carbon-sensitive consumers that adapt their consumption based on an average carbon intensity signal. This analysis represents an idealized situation for carbon-sensitive loads, where their carbon preferences are reflected directly in the market clearing, and contrasts with current practice where carbon intensity signals only become known to consumers aposteriori (i.e. after the market has already been cleared). We include both illustrative examples and larger numerical simulations, including benchmarking with other methods, to illuminate the contributions and limitations of carbon-sensitive loads in power system emission reductions.
Compositional design for time-varying and nonlinear coordination
Authors: Jonas Hansson, Emma Tegling
Date: 2025-04-09 00:00:00

This work addresses the design of multi-agent coordination through high-order consensus protocols. While first-order consensus strategies are well-studied -- with known robustness to uncertainties such as time delays, time-varying weights, and nonlinearities like saturations -- the theoretical guarantees for high-order consensus are comparatively limited. We propose a compositional control framework that generates high-order consensus protocols by serially connecting stable first-order consensus operators. Under mild assumptions, we establish that the resulting high-order system inherits stability properties from its components. The proposed design is versatile and supports a wide range of real-world constraints. This is demonstrated through applications inspired by vehicular formation control, including protocols with time-varying weights, bounded time-varying delays, and saturated inputs. We derive theoretical guarantees for these settings using the proposed compositional approach and demonstrate the advantages gained compared to conventional protocols in simulations.
Multi-Agent Trustworthy Consensus under Random Dynamic Attacks
Authors: Orhan Eren Akgün, Sarper Aydin, Stephanie Gil, Angelia Nedić
Date: 2025-04-09 00:00:00

In this work, we study the consensus problem in which legitimate agents send their values over an undirected communication network in the presence of an unknown subset of malicious or faulty agents. In contrast to former works, we generalize and characterize the properties of consensus dynamics with dependent sequences of malicious transmissions with dynamic (time-varying) rates, based on not necessarily independent trust observations. We consider a detection algorithm utilizing stochastic trust observations available to legitimate agents. Under these conditions, legitimate agents almost surely classify their neighbors and form their trusted neighborhoods correctly with decaying misclassification probabilities. We further prove that the consensus process converges almost surely despite the existence of malicious agents. For a given value of failure probability, we characterize the deviation from the nominal consensus value ideally occurring when there are no malicious agents in the system. We also examine the convergence rate of the process in finite time. Numerical simulations show the convergence among agents and indicate the deviation under different attack scenarios.