pwc-archive/papers-with-abstracts huggingface dataset:
Toward Effective AI Governance: A Review of Principles
Authors: Danilo Ribeiro, Thayssa Rocha, Gustavo Pinto, Bruno Cartaxo, Marcelo Amaral, Nicole Davila, Ana Camargo
Date: 2025-05-29 00:00:00

Artificial Intelligence (AI) governance is the practice of establishing frameworks, policies, and procedures to ensure the responsible, ethical, and safe development and deployment of AI systems. Although AI governance is a core pillar of Responsible AI, current literature still lacks synthesis across such governance frameworks and practices. Objective: To identify which frameworks, principles, mechanisms, and stakeholder roles are emphasized in secondary literature on AI governance. Method: We conducted a rapid tertiary review of nine peer-reviewed secondary studies from IEEE and ACM (20202024), using structured inclusion criteria and thematic semantic synthesis. Results: The most cited frameworks include the EU AI Act and NIST RMF; transparency and accountability are the most common principles. Few reviews detail actionable governance mechanisms or stakeholder strategies. Conclusion: The review consolidates key directions in AI governance and highlights gaps in empirical validation and inclusivity. Findings inform both academic inquiry and practical adoption in organizations.
Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Authors: Mingzhe Du, Luu Anh Tuan, Yue Liu, Yuhao QING, Dong Huang, Xinyi He, Qian Liu, Zejun Ma, See-Kiong Ng
Date: 2025-05-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO). Experiments on our Venus dataset and the APPS benchmark show that SFT and DPO rapidly saturate in efficiency gains. In contrast, GRPO, using reinforcement learning (RL) with execution feedback, continuously optimizes code performance, significantly boosting both pass@1 (from 47% to 62%) and the likelihood of outperforming human submissions in efficiency (from 31% to 45%). Our work demonstrates effective test-time code efficiency improvement and critically reveals the power of RL in teaching LLMs to truly self-improve code efficiency.
OSS-UAgent: An Agent-based Usability Evaluation Framework for Open Source Software
Authors: Lingkai Meng, Yu Shao, Long Yuan, Longbin Lai, Peng Cheng, Wenyuan Yu, Wenjie Zhang, Xuemin Lin, Jingren Zhou
Date: 2025-05-29 00:00:00
Tasks: Code Generation

Usability evaluation is critical to the impact and adoption of open source software (OSS), yet traditional methods relying on human evaluators suffer from high costs and limited scalability. To address these limitations, we introduce OSS-UAgent, an automated, configurable, and interactive agent-based usability evaluation framework specifically designed for open source software. Our framework employs intelligent agents powered by large language models (LLMs) to simulate developers performing programming tasks across various experience levels (from Junior to Expert). By dynamically constructing platform-specific knowledge bases, OSS-UAgent ensures accurate and context-aware code generation. The generated code is automatically evaluated across multiple dimensions, including compliance, correctness, and readability, providing a comprehensive measure of the software's usability. Additionally, our demonstration showcases OSS-UAgent's practical application in evaluating graph analytics platforms, highlighting its effectiveness in automating usability evaluation.
Two Is Better Than One: Rotations Scale LoRAs
Authors: Hongcan Guo, Guoshun Nan, Yuan Yang, Diyang Zhang, Haotian Li, Zhican Chen, Qinchuan Zhou, Yuhan Ran, Xinye Cao, Sicong Leng, Xiaofeng Tao, Xudong Jiang
Date: 2025-05-29 00:00:00
Tasks: Mixture-of-Experts

Scaling Low-Rank Adaptation (LoRA)-based Mixture-of-Experts (MoE) facilitates large language models (LLMs) to efficiently adapt to diverse tasks. However, traditional gating mechanisms that route inputs to the best experts may fundamentally hinder LLMs' scalability, leading to poor generalization and underfitting issues. We identify that the root cause lies in the restricted expressiveness of existing weighted-sum mechanisms, both within and outside the convex cone of LoRA representations. This motivates us to propose RadarGate, a novel geometrically inspired gating method that introduces rotational operations of LoRAs representations to boost the expressiveness and facilitate richer feature interactions among multiple LoRAs for scalable LLMs. Specifically, we first fuse each LoRA representation to other LoRAs using a learnable component and then feed the output to a rotation matrix. This matrix involves learnable parameters that define the relative angular relationship between LoRA representations. Such a simple yet effective mechanism provides an extra degree of freedom, facilitating the learning of cross-LoRA synergies and properly tracking the challenging poor generalization and underfitting issues as the number of LoRA grows. Extensive experiments on 6 public benchmarks across 21 tasks show the effectiveness of our RadarGate for scaling LoRAs. We also provide valuable insights, revealing that the rotations to each pair of representations are contrastive, encouraging closer alignment of semantically similar representations during geometrical transformation while pushing distance ones further apart. We will release our code to the community.
A Tool for Generating Exceptional Behavior Tests With Large Language Models
Authors: Linghan Zhong, Samuel Yuan, Jiyang Zhang, Yu Liu, Pengyu Nie, Junyi Jessy Li, Milos Gligoric
Date: 2025-05-28 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Exceptional behavior tests (EBTs) are crucial in software development for verifying that code correctly handles unwanted events and throws appropriate exceptions. However, prior research has shown that developers often prioritize testing "happy paths", e.g., paths without unwanted events over exceptional scenarios. We present exLong, a framework that automatically generates EBTs to address this gap. exLong leverages a large language model (LLM) fine-tuned from CodeLlama and incorporates reasoning about exception-throwing traces, conditional expressions that guard throw statements, and non-exceptional behavior tests that execute similar traces. Our demonstration video illustrates how exLong can effectively assist developers in creating comprehensive EBTs for their project (available at https://youtu.be/Jro8kMgplZk).
Recovering Fairness Directly from Modularity: a New Way for Fair Community Partitioning
Authors: Yufeng Wang, Yiguang Bai, Tianqing Zhu, Ismail Ben Ayed, Jing Yuan
Date: 2025-05-27 00:00:00
Tasks: Fairness

Community partitioning is crucial in network analysis, with modularity optimization being the prevailing technique. However, traditional modularity-based methods often overlook fairness, a critical aspect in real-world applications. To address this, we introduce protected group networks and propose a novel fairness-modularity metric. This metric extends traditional modularity by explicitly incorporating fairness, and we prove that minimizing it yields naturally fair partitions for protected groups while maintaining theoretical soundness. We develop a general optimization framework for fairness partitioning and design the efficient Fair Fast Newman (FairFN) algorithm, enhancing the Fast Newman (FN) method to optimize both modularity and fairness. Experiments show FairFN achieves significantly improved fairness and high-quality partitions compared to state-of-the-art methods, especially on unbalanced datasets.
Decoupling Periodic Systems: An Algebraic Approach
Authors: Vladimír Kučera
Date: 2025-05-29 00:00:00

This paper addresses the problem of row-by-row (or diagonal) decoupling of discrete-time linear multi-input multi-output systems with periodic time-varying coefficients using periodic state feedback. Previous solutions have tackled row-by-row decoupling using dynamic compensation for square systems and block-decoupling through regular state feedback for nonsquare systems with more outputs than inputs. While it appears likely that a row-by-row state feedback solution for square systems can be deduced from these findings, a direct argument seems more appropriate here as it presents a natural extension for decoupling nonsquare systems with more inputs than outputs. This extension, which necessitates nonregular state feedback, has yet to be explored for periodic systems. Our approach is purely algebraic, based on a time-invariant representation of the periodic system.
Integrated design of system structure and delayed resonator towards efficient non-collocated vibration absorption
Authors: Adam Peichl, Matěj Kuře, Wim Michiels, Tomáš Vyhlídal
Date: 2025-05-29 00:00:00

The problem of non-collocated vibration absorption by a delayed resonator is addressed with emphasis on system fatigue resistance and energy efficiency of control actions. The analysis is performed for a system consisting of an arbitrary large series of flexibly linked single-degree-of-freedom masses. For the stage where the vibration of the target mass is fully absorbed by the non-collocated resonator, key forces, motion amplitudes and potential energies across the system structure are assessed. Next, a complete parameter set of the resonator gain and delay is derived, and the actuation force and power needed by the resonator for the full vibration absorption is determined. The derived quantities are utilized in forming an optimization problem to balance minimal risk of fatigue across the system structure and power needed by the resonator, under the closed loop stability and parameter constraints. Next to the gain and delay of the resonator, selected structural parameters of the system are used as variables in the constrained nonlinear optimization problem. Experimental and numerical case studies are included to demonstrate benefits of the proposed integrated structural and control design.
Robust Aperiodic Sampled-Data Washout Control for Uncertain Affine Systems
Authors: Folco Giorgetti, Francesco Crocetti, Mario Luca Fravolini, Francesco Ferrante
Date: 2025-05-29 00:00:00

In this paper, we address the problem of designing an aperiodic sampled-data controller stabilizing the zero-input equilibrium of an uncertain affine plant. The closed-loop system is modeled as a hybrid dynamical system incorporating a timer triggering the occurrence of the sampling events and two memory states storing the value of the controller state and controller output at each sampling time. Necessary and sufficient conditions on the controller parameters are given to establish the sought property. A constructive controller design algorithm based on sum-of-squares programming is given. A numerical example illustrates the effectiveness of the approach.
Evaluation of Voltage Unbalance Metrics in Distribution Networks with High DER Penetration
Authors: Alireza Zabihi, Luis Badesa, Araceli Hernandez
Date: 2025-05-29 00:00:00

Voltage unbalance, caused by variations in voltage magnitude and phase angle, is a significant power quality issue in three-phase systems, leading to equipment inefficiencies and increased system losses. The integration of distributed energy resources (DER) into the grid adds complexity, as DER can either reduce or worsen voltage unbalance, depending on factors such as grid configuration and the distribution of loads and DER themselves. This study explores the effects of DER penetration on voltage unbalance levels and the accuracy of the different indices most commonly used to quantify this unbalance. The results highlight the varying impacts of DER on unbalance and index performance, emphasizing the need for effective strategies to assess voltage unbalance in modern distribution systems.
CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection
Authors: Woojin Shin, Donghwa Kang, Byeongyun Park, Brent ByungHoon Kang, Jinkyu Lee, Hyeongboo Baek
Date: 2025-05-29 00:00:00
Tasks: GPU, object-detection, Object Detection, Real-Time Object Detection, Scheduling

Detection Transformers (DETR) are increasingly adopted in autonomous vehicle (AV) perception systems due to their superior accuracy over convolutional networks. However, concurrently executing multiple DETR tasks presents significant challenges in meeting firm real-time deadlines (R1) and high accuracy requirements (R2), particularly for safety-critical objects, while navigating the inherent latency-accuracy trade-off under resource constraints. Existing real-time DNN scheduling approaches often treat models generically, failing to leverage Transformer-specific properties for efficient resource allocation. To address these challenges, we propose CF-DETR, an integrated system featuring a novel coarse-to-fine Transformer architecture and a dedicated real-time scheduling framework NPFP**. CF-DETR employs three key strategies (A1: coarse-to-fine inference, A2: selective fine inference, A3: multi-level batch inference) that exploit Transformer properties to dynamically adjust patch granularity and attention scope based on object criticality, aiming to satisfy R2. The NPFP** scheduling framework (A4) orchestrates these adaptive mechanisms A1-A3. It partitions each DETR task into a safety-critical coarse subtask for guaranteed critical object detection within its deadline (ensuring R1), and an optional fine subtask for enhanced overall accuracy (R2), while managing individual and batched execution. Our extensive evaluations on server, GPU-enabled embedded platforms, and actual AV platforms demonstrate that CF-DETR, under an NPFP** policy, successfully meets strict timing guarantees for critical operations and achieves significantly higher overall and critical object detection accuracy compared to existing baselines across diverse AV workloads.
Optimizing Connectivity and Scheduling of Near/Far Field Users in Massive MIMO NOMA System
Authors: Ziad Qais Al-Abbasi
Date: 2025-05-29 00:00:00
Tasks: Clustering, Fairness, Scheduling

It is envisioned that the next generations of wireless communication environment will be characterized with dense traffic demand due to the prediction that there will be large numbers of active users. Hence, it is important to find a solution to deal with such dense numbers of users. This paper investigates optimizing the connectivity and users scheduling to improve the performance of near and far field users in a downlink, multiuser, massive MIMO-NOMA system. For the considered system model, combining NOMA side by side with massive MIMO offers a great opportunity to exploit the available radio resources and boost the overall system efficiency. The paper proposes separate clustering of near field users and far field users. It also proposes using a beamforming scheme to separately serve the users within each cluster. However, NOMA is proposed to be applied among all users to boost resource sharing. In particular, a cognitive-NOMA beamforming scheme and NOMA themed beamforming are proposed to serve the users within each cluster, and they are compared against random beamforming from literature. Simulation results show that both of the proposed beamforming schemes proved their superiority as compared to random beamforming. Several scheduling techniques were also considered in this paper to examine possible solutions for boosting the system performance considered, namely, priority, joint, dynamic, and fairness-based scheduling techniques for both near field and far field users. The paper also proposes a suboptimal, fairness aiming and gradual allocation approach for allocating the transmission power among the users. The results show that user-clustering offers better connectivity and scheduling performance than the case where no clustering is applied.
Latent Representations for Control Design with Provable Stability and Safety Guarantees
Authors: Paul Lutkus, Kaiyuan Wang, Lars Lindemann, Stephen Tu
Date: 2025-05-29 00:00:00
Tasks: Collision Avoidance

We initiate a formal study on the use of low-dimensional latent representations of dynamical systems for verifiable control synthesis. Our main goal is to enable the application of verification techniques -- such as Lyapunov or barrier functions -- that might otherwise be computationally prohibitive when applied directly to the full state representation. Towards this goal, we first provide dynamics-aware approximate conjugacy conditions which formalize the notion of reconstruction error necessary for systems analysis. We then utilize our conjugacy conditions to transfer the stability and invariance guarantees of a latent certificate function (e.g., a Lyapunov or barrier function) for a latent space controller back to the original system. Importantly, our analysis contains several important implications for learning latent spaces and dynamics, by highlighting the necessary geometric properties which need to be preserved by the latent space, in addition to providing concrete loss functions for dynamics reconstruction that are directly related to control design. We conclude by demonstrating the applicability of our theory to two case studies: (1) stabilization of a cartpole system, and (2) collision avoidance for a two vehicle system.
Interturn Fault Detection in IPMSMs: Two Adaptive Observer-based Solutions
Authors: Romeo Ortega, Alexey Bobtsov, Leyan Fang, Oscar Texis-Loaiza, Johannes Schiffer
Date: 2025-05-29 00:00:00
Tasks: Fault Detection, parameter estimation

In this paper we address the problem of online detection of inter-turn short-circuit faults (ITSCFs) that occur in permanent magnet synchronous motors (PMSMs). We propose two solutions to this problem: (i) a very simple linear observer and (ii) a generalized parameter estimation based observer, that incorporates a high performance estimator -- with both observers detecting the short-circuit current and the fault intensity. Although the first solution guarantees the detection of the fault exponentially fast, the rate of convergence is fully determined by the motor parameters that, in some cases, may be too slow. The second observer, on the other hand, ensures finite convergence time under the weakest assumption of interval excitation. To make the observers adaptive, we develop a parameter estimator that, in the case of isotropic PMSMs, estimates on-line (exponentially fast) the resistance and inductance of the motor. It should be underscored that, in contrast with existing observers (including the widely popular Kalman filter) that provide indirect information of the fault current, our observers provide explicit one -- namely the amplitude of the fault current. The performance of both observers, in their linear and generalized parameter estimation-based versions, is illustrated with realistic simulation studies.
Voltage Control of the Boost Converter: PI vs. Nonlinear Passivity-based Control
Authors: Leyan Fang, Romeo Ortega, Robert Griñó
Date: 2025-05-29 00:00:00

We carry-out a detailed analysis of direct voltage control of a Boost converter feeding a simple resistive load. First, we prove that using a classical PI control to stabilize a desired equilibrium leads to a very complicated dynamic behavior consisting of two equilibrium points, one of them always unstable for all PI gains and circuit parameter values. Interestingly, the second equilibrium point may be rendered stable -- but for all tuning gains leading to an extremely large value of the circuit current and the controller integrator state. Moreover, if we neglect the resistive effect of the inductor, there is only one equilibrium and it is always unstable. From a practical point of view, it is important to note that the only useful equilibrium point is that of minimum current and that, in addition, there is always a resistive component in the inductor either by its parasitic resistance or by the resistive component of the output impedance of the previous stage. In opposition to this troublesome scenario we recall three nonlinear voltage-feedback controllers, that ensure asymptotic stability of the desired equilibrium with simple gain tuning rules, an easily defined domain of attraction and smooth transient behavior. Two of them are very simple, nonlinear, static voltage feedback rules, while the third one is a variation of the PID scheme called PID-Passivity-based Control (PBC). In its original formulation PID-PBC requires full state measurement, but we present a modified version that incorporates a current observer. All three nonlinear controllers are designed following the principles of PBC, which has had enormous success in many engineering applications.
Detecting Switching Attacks On Traffic Flow Regulation For Changing Driving Patterns
Authors: Sanchita Ghosh, Tanushree Roy
Date: 2025-05-29 00:00:00

Modern traffic management systems increasingly adopt hierarchical control strategies for improved efficiency and scalability, where a local traffic controller mode is chosen by a supervisory controller based on the changing large-scale driving patterns. Unfortunately, such local metering controllers are also vulnerable to cyberattacks that can disrupt the controller switching, leading to undesired, inefficient, and even unsafe traffic operations. Additionally, the detection of such attacks becomes challenging when the operational mode of the traffic is uncertain and the operational mode identification is delayed. Thus, in this work, we propose a cyberattack detection scheme to detect the compromised controller switching in ramp metering for an uncertain, multimodal macroscopic traffic operation of a freeway segment. In particular, we propose a bank of detectors corresponding to each admissible traffic mode that can compensate for the uncertain traffic mode of the freeway. Furthermore, we utilize backstepping tools along with Lyapunov function theory to achieve analytical performance guarantees for the detector, such as nominal exponential stability, anomaly/uncertainty-to-residual stability, robustness, and sensitivity. Finally, we demonstrate the efficacy of the proposed detection scheme through simulations of free traffic under realistic traffic parameters, uncertainties, and commonly occurring attack scenarios.
Sensitivity of DC Network Representation for GIC Analysis
Authors: Aniruddh Mishra, Arthur K. Barnes, Jose E. Tabarez, Adam Mate
Date: 2025-05-29 00:00:00
Tasks: Blocking, Sensitivity

Geomagnetic disturbances are a threat to the reliability and security of our national critical energy infrastructures. These events specifically result in geomagnetically induced currents, which can cause damage to transformers due to magnetic saturation. In order to mitigate these effects, blocker devices must be placed in optimal locations. Finding this placement requires a dc representation of the ac transmission lines, which this paper discusses. Different decisions in this process, including the method of representing the blocking devices, result in significant variations to the power loss calculations. To analyze these effects, we conclude the paper by comparing the losses on a sample network with different modeling implementations.