pwc-archive/papers-with-abstracts huggingface dataset:
Are EVs Cleaner Than We Think? Evaluating Consequential Greenhouse Gas Emissions from EV Charging
Authors: Riti Bhandarkar, Qian Luo, Emil Dimanchev, Jesse D. Jenkins
Date: 2025-04-24 00:00:00

While electrifying transportation eliminates tailpipe greenhouse gas (GHG) emissions, electric vehicle (EV) adoption can create additional electricity sector emissions. To quantify this emissions impact, prior work typically employs short-run marginal emissions or average emissions rates calculated from historical data or power systems models that do not consider changes in installed capacity. In this work, we use an electricity system capacity expansion model to consider the full consequential GHG emissions impact from large-scale EV adoption in the western United States, accounting for induced changes in generation and storage capacity. We find that the metrics described above do not accurately reflect the true emissions impact of EV adoption-average emissions rates can either under- or over-estimate emission impacts, and short-run marginal emissions rates can significantly underestimate emission reductions, especially when charging timing is flexible. Our results also show that using short-run marginal emission rates as signals to coordinate EV charging could increase emissions relative to price-based charging signals, indicating the need for alternative control strategies to minimize consequential emissions.
SAPO-RL: Sequential Actuator Placement Optimization for Fuselage Assembly via Reinforcement Learning
Authors: Peng Ye, Juan Du
Date: 2025-04-24 00:00:00
Tasks: Decision Making, Q-Learning, Reinforcement Learning (RL), Sequential Decision Making

Precise assembly of composite fuselages is critical for aircraft assembly to meet the ultra-high precision requirements. Due to dimensional variations, there is a gap when two fuselage assemble. In practice, actuators are required to adjust fuselage dimensions by applying forces to specific points on fuselage edge through pulling or pushing force actions. The positioning and force settings of these actuators significantly influence the efficiency of the shape adjustments. The current literature usually predetermines the fixed number of actuators, which is not optimal in terms of overall quality and corresponding actuator costs. However, optimal placement of actuators in terms of both locations and number is challenging due to compliant structures, complex material properties, and dimensional variabilities of incoming fuselages. To address these challenges, this paper introduces a reinforcement learning (RL) framework that enables sequential decision-making for actuator placement selection and optimal force computation. Specifically, our methodology employs the Dueling Double Deep Q-Learning (D3QN) algorithm to refine the decision-making capabilities of sequential actuator placements. The environment is meticulously crafted to enable sequential and incremental selection of an actuator based on system states. We formulate the actuator selection problem as a submodular function optimization problem, where the sub-modularity properties can be adopted to efficiently achieve near-optimal solutions. The proposed methodology has been comprehensively evaluated through numerical studies and comparison studies, demonstrating its effectiveness and outstanding performance in enhancing assembly precision with limited actuator numbers.
From Precision to Perception: User-Centred Evaluation of Keyword Extraction Algorithms for Internet-Scale Contextual Advertising
Authors: Jingwen Cai, Sara Leckner, Johanna Björklund
Date: 2025-04-30 00:00:00
Tasks: Benchmarking, Computational Efficiency, Keyword Extraction

Keyword extraction is a foundational task in natural language processing, underpinning countless real-world applications. A salient example is contextual advertising, where keywords help predict the topical congruence between ads and their surrounding media contexts to enhance advertising effectiveness. Recent advances in artificial intelligence, particularly large language models, have improved keyword extraction capabilities but also introduced concerns about computational cost. Moreover, although the end-user experience is of vital importance, human evaluation of keyword extraction performances remains under-explored. This study provides a comparative evaluation of three prevalent keyword extraction algorithms that vary in complexity: TF-IDF, KeyBERT, and Llama 2. To evaluate their effectiveness, a mixed-methods approach is employed, combining quantitative benchmarking with qualitative assessments from 552 participants through three survey-based experiments. Findings indicate a slight user preference for KeyBERT, which offers a favourable balance between performance and computational efficiency compared to the other two algorithms. Despite a strong overall preference for gold-standard keywords, differences between the algorithmic outputs are not statistically significant, highlighting a long-overlooked gap between traditional precision-focused metrics and user-perceived algorithm efficiency. The study highlights the importance of user-centred evaluation methodologies and proposes analytical tools to support their implementation.
On the Eigenvalue Tracking of Large-Scale Systems
Authors: Andreas Bouterakos, Joseph McKeon, Georgios Tzounas
Date: 2025-04-24 00:00:00
Tasks: Numerical Integration

The paper focuses on the problem of tracking eigenvalue trajectories in large-scale power system models as system parameters vary. A continuation-based formulation is presented for tracing any single eigenvalue of interest, which supports sparse matrix representations and accommodates both explicit and semi-implicit differential-algebraic models. Key implementation aspects, such as numerical integration, matrix updates, derivative approximations, and handling defective eigenvalues, are discussed in detail and practical recommendations are duly provided. The tracking approach is demonstrated through a comprehensive case study on the IEEE 39-bus system, as well as on a realistic dynamic model of the Irish transmission system.
Effective Inference-Free Retrieval for Learned Sparse Representations
Authors: Franco Maria Nardini, Thong Nguyen, Cosimo Rulli, Rossano Venturini, Andrew Yates
Date: 2025-04-30 00:00:00
Tasks: Domain Generalization, Retrieval

Learned Sparse Retrieval (LSR) is an effective IR approach that exploits pre-trained language models for encoding text into a learned bag of words. Several efforts in the literature have shown that sparsity is key to enabling a good trade-off between the efficiency and effectiveness of the query processor. To induce the right degree of sparsity, researchers typically use regularization techniques when training LSR models. Recently, new efficient -- inverted index-based -- retrieval engines have been proposed, leading to a natural question: has the role of regularization changed in training LSR models? In this paper, we conduct an extended evaluation of regularization approaches for LSR where we discuss their effectiveness, efficiency, and out-of-domain generalization capabilities. We first show that regularization can be relaxed to produce more effective LSR encoders. We also show that query encoding is now the bottleneck limiting the overall query processor performance. To remove this bottleneck, we advance the state-of-the-art of inference-free LSR by proposing Learned Inference-free Retrieval (Li-LSR). At training time, Li-LSR learns a score for each token, casting the query encoding step into a seamless table lookup. Our approach yields state-of-the-art effectiveness for both in-domain and out-of-domain evaluation, surpassing Splade-v3-Doc by 1 point of mRR@10 on MS MARCO and 1.8 points of nDCG@10 on BEIR.
One-Point Sampling for Distributed Bandit Convex Optimization with Time-Varying Constraints
Authors: Kunpeng Zhang, Lei Xu, Xinlei Yi, Guanghui Wen, Lihua Xie, Tianyou Chai, Tao Yang
Date: 2025-04-22 00:00:00

This paper considers the distributed bandit convex optimization problem with time-varying constraints. In this problem, the global loss function is the average of all the local convex loss functions, which are unknown beforehand. Each agent iteratively makes its own decision subject to time-varying inequality constraints which can be violated but are fulfilled in the long run. For a uniformly jointly strongly connected time-varying directed graph, a distributed bandit online primal-dual projection algorithm with one-point sampling is proposed. We show that sublinear dynamic network regret and network cumulative constraint violation are achieved if the path-length of the benchmark also increases in a sublinear manner. In addition, an $\mathcal{O}({T^{3/4 + g}})$ static network regret bound and an $\mathcal{O}( {{T^{1 - {g}/2}}} )$ network cumulative constraint violation bound are established, where $T$ is the total number of iterations and $g \in ( {0,1/4} )$ is a trade-off parameter. Moreover, a reduced static network regret bound $\mathcal{O}( {{T^{2/3 + 4g /3}}} )$ is established for strongly convex local loss functions. Finally, a numerical example is presented to validate the theoretical results.
In a Few Words: Comparing Weak Supervision and LLMs for Short Query Intent Classification
Authors: Daria Alexander, Arjen P. de Vries
Date: 2025-04-30 00:00:00
Tasks: Classification, In-Context Learning, Information Retrieval, intent-classification, Intent Classification

User intent classification is an important task in information retrieval. Previously, user intents were classified manually and automatically; the latter helped to avoid hand labelling of large datasets. Recent studies explored whether LLMs can reliably determine user intent. However, researchers have recognized the limitations of using generative LLMs for classification tasks. In this study, we empirically compare user intent classification into informational, navigational, and transactional categories, using weak supervision and LLMs. Specifically, we evaluate LLaMA-3.1-8B-Instruct and LLaMA-3.1-70B-Instruct for in-context learning and LLaMA-3.1-8B-Instruct for fine-tuning, comparing their performance to an established baseline classifier trained using weak supervision (ORCAS-I). Our results indicate that while LLMs outperform weak supervision in recall, they continue to struggle with precision, which shows the need for improved methods to balance both metrics effectively.
Admittance Identification of Grid-Forming Inverters Using Time and Frequency-Domain Techniques
Authors: Andres Intriago, Alexandros Paspatis, Francesco Liberati, Charalambos Konstantinou
Date: 2025-04-24 00:00:00

The increasing integration of inverter-based resources (IBRs) into the power grid introduces new challenges, requiring detailed electromagnetic transient (EMT) studies to analyze system interactions. Despite these needs, access to the internal firmware of power electronic devices remains restricted due to stringent nondisclosure agreements enforced by manufacturers. To address this, we explore three system identification techniques: sweep frequency response analysis (SFRA), step excitation method (SEM), and eigensystem realization algorithm (ERA). SFRA employs sinusoidal signals of varying frequencies to measure the system's frequency response, while SEM and ERA utilize step functions to derive time-domain responses and transform them into Laplace-domain transfer functions. All three approaches are shown to provide consistent results in identifying the dq admittance of grid-forming inverters (GFM) over a frequency range of 1 Hz to 100 Hz.
FIM: Frequency-Aware Multi-View Interest Modeling for Local-Life Service Recommendation
Authors: Guoquan Wang, Qiang Luo, Weisong Hu, Pengfei Yao, Wencong Zeng, Guorui Zhou, Kun Gai
Date: 2025-04-23 00:00:00
Tasks: Recommendation Systems

People's daily lives involve numerous periodic behaviors, such as eating and traveling. Local-life platforms cater to these recurring needs by providing essential services tied to daily routines. Therefore, users' periodic intentions are reflected in their interactions with the platforms. There are two main challenges in modeling users' periodic behaviors in the local-life service recommendation systems: 1) the diverse demands of users exhibit varying periodicities, which are difficult to distinguish as they are mixed in the behavior sequences; 2) the periodic behaviors of users are subject to dynamic changes due to factors such as holidays and promotional events. Existing methods struggle to distinguish the periodicities of diverse demands and overlook the importance of dynamically capturing changes in users' periodic behaviors. To this end, we employ a Frequency-Aware Multi-View Interest Modeling framework (FIM). Specifically, we propose a multi-view search strategy that decomposes users' demands from different perspectives to separate their various periodic intentions. This allows the model to comprehensively extract their periodic features than category-searched-only methods. Moreover, we propose a frequency-domain perception and evolution module. This module uses the Fourier Transform to convert users' temporal behaviors into the frequency domain, enabling the model to dynamically perceive their periodic features. Extensive offline experiments demonstrate that FIM achieves significant improvements on public and industrial datasets, showing its capability to effectively model users' periodic intentions. Furthermore, the model has been deployed on the Kuaishou local-life service platform. Through online A/B experiments, the transaction volume has been significantly improved.
Longitudinal Control for Autonomous Racing with Combustion Engine Vehicles
Authors: Phillip Pitschi, Simon Sagmeister, Sven Goblirsch, Markus Lienkamp, Boris Lohmann
Date: 2025-04-24 00:00:00
Tasks: Autonomous Driving, Autonomous Racing

Usually, a controller for path- or trajectory tracking is employed in autonomous driving. Typically, these controllers generate high-level commands like longitudinal acceleration or force. However, vehicles with combustion engines expect different actuation inputs. This paper proposes a longitudinal control concept that translates high-level trajectory-tracking commands to the required low-level vehicle commands such as throttle, brake pressure and a desired gear. We chose a modular structure to easily integrate different trajectory-tracking control algorithms and vehicles. The proposed control concept enables a close tracking of the high-level control command. An anti-lock braking system, traction control, and brake warmup control also ensure a safe operation during real-world tests. We provide experimental validation of our concept using real world data with longitudinal accelerations reaching up to $25 \, \frac{\mathrm{m}}{\mathrm{s}^2}$. The experiments were conducted using the EAV24 racecar during the first event of the Abu Dhabi Autonomous Racing League on the Yas Marina Formula 1 Circuit.
Enhancing New-item Fairness in Dynamic Recommender Systems
Authors: Huizhong Guo, Zhu Sun, Dongxia Wang, Tianjun Wei, Jinfeng Li, Jie Zhang
Date: 2025-04-30 00:00:00
Tasks: Fairness, Knowledge Distillation, Recommendation Systems, Reinforcement Learning (RL)

New-items play a crucial role in recommender systems (RSs) for delivering fresh and engaging user experiences. However, traditional methods struggle to effectively recommend new-items due to their short exposure time and limited interaction records, especially in dynamic recommender systems (DRSs) where new-items get continuously introduced and users' preferences evolve over time. This leads to significant unfairness towards new-items, which could accumulate over the successive model updates, ultimately compromising the stability of the entire system. Therefore, we propose FairAgent, a reinforcement learning (RL)-based new-item fairness enhancement framework specifically designed for DRSs. It leverages knowledge distillation to extract collaborative signals from traditional models, retaining strong recommendation capabilities for old-items. In addition, FairAgent introduces a novel reward mechanism for recommendation tailored to the characteristics of DRSs, which consists of three components: 1) a new-item exploration reward to promote the exposure of dynamically introduced new-items, 2) a fairness reward to adapt to users' personalized fairness requirements for new-items, and 3) an accuracy reward which leverages users' dynamic feedback to enhance recommendation accuracy. Extensive experiments on three public datasets and backbone models demonstrate the superior performance of FairAgent. The results present that FairAgent can effectively boost new-item exposure, achieve personalized new-item fairness, while maintaining high recommendation accuracy.
Finding Conditions for Target Controllability under Christmas Trees
Authors: Marco Peruzzo, Giacomo Baggio, Francesco Ticozzi
Date: 2025-04-24 00:00:00

This paper presents new graph-theoretic conditions for structural target controllability of directed networks. After reviewing existing conditions and highlighting some gaps in the literature, we introduce a new class of network systems, named Christmas trees, which generalizes trees and cacti. We then establish a graph-theoretic characterization of sets of nodes that are structurally target controllable for a simple subclass of Christmas trees. Our characterization applies to general network systems by considering spanning subgraphs of Christmas tree class and allows us to uncover target controllable sets that existing criteria fail to identify.
A Framework for Elastic Adaptation of User Multiple Intents in Sequential Recommendation
Authors: Zhikai Wang, Yanyan Shen
Date: 2025-04-30 00:00:00
Tasks: Incremental Learning, Sequential Recommendation

Recently, substantial research has been conducted on sequential recommendation, with the objective of forecasting the subsequent item by leveraging a user's historical sequence of interacted items. Prior studies employ both capsule networks and self-attention techniques to effectively capture diverse underlying intents within a user's interaction sequence, thereby achieving the most advanced performance in sequential recommendation. However, users could potentially form novel intents from fresh interactions as the lengths of user interaction sequences grow. Consequently, models need to be continually updated or even extended to adeptly encompass these emerging user intents, referred as incremental multi-intent sequential recommendation. % We refer to this problem as incremental multi-intent sequential recommendation, which has not yet been well investigated in the existing literature. In this paper, we propose an effective Incremental learning framework for user Multi-intent Adaptation in sequential recommendation called IMA, which augments the traditional fine-tuning strategy with the existing-intents retainer, new-intents detector, and projection-based intents trimmer to adaptively expand the model to accommodate user's new intents and prevent it from forgetting user's existing intents. Furthermore, we upgrade the IMA into an Elastic Multi-intent Adaptation (EMA) framework which can elastically remove inactive intents and compress user intent vectors under memory space limit. Extensive experiments on real-world datasets verify the effectiveness of the proposed IMA and EMA on incremental multi-intent sequential recommendation, compared with various baselines.
Analysis and Mitigation of Data injection Attacks against Data-Driven Control
Authors: Sribalaji C. Anand
Date: 2025-04-24 00:00:00

This paper investigates the impact of false data injection attacks on data-driven control systems. Specifically, we consider an adversary injecting false data into the sensor channels during the learning phase. When the operator seeks to learn a stable state-feedback controller, we propose an attack strategy capable of misleading the operator into learning an unstable feedback gain. We also investigate the effects of constant-bias injection attacks on data-driven linear quadratic regulation (LQR). Finally, we explore potential mitigation strategies and support our findings with numerical examples.
Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)
Authors: Keyan Miao, Liqun Zhao, Han Wang, Konstantinos Gatsis, Antonis Papachristodoulou
Date: 2025-04-23 00:00:00

Designing controllers that achieve task objectives while ensuring safety is a key challenge in control systems. This work introduces Opt-ODENet, a Neural ODE framework with a differentiable Quadratic Programming (QP) optimization layer to enforce constraints as hard requirements. Eliminating the reliance on nominal controllers or large datasets, our framework solves the optimal control problem directly using Neural ODEs. Stability and convergence are ensured through Control Lyapunov Functions (CLFs) in the loss function, while Control Barrier Functions (CBFs) embedded in the QP layer enforce real-time safety. By integrating the differentiable QP layer with Neural ODEs, we demonstrate compatibility with the adjoint method for gradient computation, enabling the learning of the CBF class-$\mathcal{K}$ function and control network parameters. Experiments validate its effectiveness in balancing safety and performance.
Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems
Authors: Haoyu Li, Xiangru Zhong, Bin Hu, huan zhang
Date: 2025-04-23 00:00:00

Contraction metrics are crucial in control theory because they provide a powerful framework for analyzing stability, robustness, and convergence of various dynamical systems. However, identifying these metrics for complex nonlinear systems remains an open challenge due to the lack of scalable and effective tools. This paper explores the approach of learning verifiable contraction metrics parametrized as neural networks (NNs) for discrete-time nonlinear dynamical systems. While prior works on formal verification of contraction metrics for general nonlinear systems have focused on convex optimization methods (e.g. linear matrix inequalities, etc) under the assumption of continuously differentiable dynamics, the growing prevalence of NN-based controllers, often utilizing ReLU activations, introduces challenges due to the non-smooth nature of the resulting closed-loop dynamics. To bridge this gap, we establish a new sufficient condition for establishing formal neural contraction metrics for general discrete-time nonlinear systems assuming only the continuity of the dynamics. We show that from a computational perspective, our sufficient condition can be efficiently verified using the state-of-the-art neural network verifier $\alpha,\!\beta$-CROWN, which scales up non-convex neural network verification via novel integration of symbolic linear bound propagation and branch-and-bound. Built upon our analysis tool, we further develop a learning method for synthesizing neural contraction metrics from sampled data. Finally, our approach is validated through the successful synthesis and verification of NN contraction metrics for various nonlinear examples.