pwc-archive/papers-with-abstracts huggingface dataset:
Learning Personalized Utility Functions for Drivers in Ride-hailing Systems Using Ensemble Hypernetworks
Authors: Weiming Mai, Jie Gao, Oded Cats
Date: 2025-06-21 00:00:00
Tasks: Ensemble Learning, Uncertainty Quantification

In ride-hailing systems, drivers decide whether to accept or reject ride requests based on factors such as order characteristics, traffic conditions, and personal preferences. Accurately predicting these decisions is essential for improving the efficiency and reliability of these systems. Traditional models, such as the Random Utility Maximization (RUM) approach, typically predict drivers' decisions by assuming linear correlations among attributes. However, these models often fall short because they fail to account for non-linear interactions between attributes and do not cater to the unique, personalized preferences of individual drivers. In this paper, we develop a method for learning personalized utility functions using hypernetwork and ensemble learning. Hypernetworks dynamically generate weights for a linear utility function based on trip request data and driver profiles, capturing the non-linear relationships. An ensemble of hypernetworks trained on different data segments further improve model adaptability and generalization by introducing controlled randomness, thereby reducing over-fitting. We validate the performance of our ensemble hypernetworks model in terms of prediction accuracy and uncertainty estimation in a real-world dataset. The results demonstrate that our approach not only accurately predicts each driver's utility but also effectively balances the needs for explainability and uncertainty quantification. Additionally, our model serves as a powerful tool for revealing the personalized preferences of different drivers, clearly illustrating which attributes largely impact their rider acceptance decisions.
Keeping Medical AI Healthy: A Review of Detection and Correction Methods for System Degradation
Authors: Hao Guan, David Bates, Li Zhou
Date: 2025-06-20 00:00:00
Tasks: Test-time Adaptation

Artificial intelligence (AI) is increasingly integrated into modern healthcare, offering powerful support for clinical decision-making. However, in real-world settings, AI systems may experience performance degradation over time, due to factors such as shifting data distributions, changes in patient characteristics, evolving clinical protocols, and variations in data quality. These factors can compromise model reliability, posing safety concerns and increasing the likelihood of inaccurate predictions or adverse outcomes. This review presents a forward-looking perspective on monitoring and maintaining the "health" of AI systems in healthcare. We highlight the urgent need for continuous performance monitoring, early degradation detection, and effective self-correction mechanisms. The paper begins by reviewing common causes of performance degradation at both data and model levels. We then summarize key techniques for detecting data and model drift, followed by an in-depth look at root cause analysis. Correction strategies are further reviewed, ranging from model retraining to test-time adaptation. Our survey spans both traditional machine learning models and state-of-the-art large language models (LLMs), offering insights into their strengths and limitations. Finally, we discuss ongoing technical challenges and propose future research directions. This work aims to guide the development of reliable, robust medical AI systems capable of sustaining safe, long-term deployment in dynamic clinical settings.
Closed-Loop Molecular Communication with Local and Global Degradation: Modeling and ISI Analysis
Authors: Lukas Brand, Fardad Vakilipoor, Sören Botsch, Timo Jakumeit, Sebastian Lotter, Robert Schober, Maximilian Schäfer
Date: 2025-06-20 00:00:00

This paper presents a novel physics-based model for signal propagation in closed-loop molecular communication (MC) systems, which are particularly relevant for many envisioned biomedical applications, such as health monitoring or drug delivery within the closed-loop human cardiovascular system (CVS). Compared to open-loop systems, which are mostly considered in MC, closed-loop systems exhibit different characteristic effects influencing signaling molecule (SM) propagation. One key phenomenon are the periodic SM arrivals at the receiver (RX), leading to various types of inter-symbol interference (ISI) inherent to closed-loop system. To capture these characteristic effects, we propose an analytical model for the SM propagation inside closed-loop systems. The model accounts for arbitrary spatio-temporal SM release patterns at the transmitter (TX), and incorporates several environmental effects such as fluid flow, SM diffusion, and SM degradation. Moreover, to capture a wide range of practically relevant degradation and clearance mechanisms, the model includes both local removal (e.g., due to SM absorption into organs) and global removal (e.g., due to chemical degradation) of SMs. The accuracy of the proposed model is validated with three-dimensional (3-D) particle-based simulations (PBSs). Moreover, we utilize the proposed model to develop a rigorous characterization of the various types of ISI encountered in closed-loop MC systems.
Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer
Authors: Runkai Zhang, Hua Yu, John Q. Gan, Haixian Wang
Date: 2025-06-20 00:00:00
Tasks: EEG, Quantization, Representation Learning

Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are vital for epilepsy diagnosis and treatment. Their unified analysis offers the potential to harness the complementary strengths of each modality but is challenging due to variations in recording montages, amplitude and signal-to-noise ratio (SNR), and frequency components. To address the aforementioned challenges, this paper introduces EpiNT, a novel Transformer-based pre-trained model for unified EEG and iEEG analysis. EpiNT employs channel-independent modeling with masked autoencoders (MAE) and vector quantization (VQ), along with a frequency domain mapping quantizer to capture crucial frequency features. Pre-trained on over 2,700 hours of multi-modal clinical neurophysiological data from 1,199 patients, EpiNT outperformed both randomly initialized models and other pre-trained methods on six downstream classification tasks, demonstrating robust representation learning capabilities. This work presents a promising approach for unified epilepsy neurophysiology analysis.
Enhancing Expressivity of Quantum Neural Networks Based on the SWAP test
Authors: Sebastian Nagies, Emiliano Tolotti, Davide Pastorello, Enrico Blanzieri
Date: 2025-06-20 00:00:00

Parameterized quantum circuits represent promising architectures for machine learning applications, yet many lack clear connections to classical models, potentially limiting their ability to translate the wide success of classical neural networks to the quantum realm. We examine a specific type of quantum neural network (QNN) built exclusively from SWAP test circuits, and discuss its mathematical equivalence to a classical two-layer feedforward network with quadratic activation functions under amplitude encoding. Our analysis across classical real-world and synthetic datasets reveals that while this architecture can successfully learn many practical tasks, it exhibits fundamental expressivity limitations due to violating the universal approximation theorem, particularly failing on harder problems like the parity check function. To address this limitation, we introduce a circuit modification using generalized SWAP test circuits that effectively implements classical neural networks with product layers. This enhancement enables successful learning of parity check functions in arbitrary dimensions which we analytically argue to be impossible for the original architecture beyond two dimensions regardless of network size. Our results establish a framework for enhancing QNN expressivity through classical task analysis and demonstrate that our SWAP test-based architecture offers broad representational capacity, suggesting potential promise also for quantum learning tasks.
Real-Time Black-Box Optimization for Dynamic Discrete Environments Using Embedded Ising Machines
Authors: Tomoya Kashimata, Yohei Hamakawa, Masaya Yamasaki, Kosuke Tatsumura
Date: 2025-06-20 00:00:00

Many real-time systems require the optimization of discrete variables. Black-box optimization (BBO) algorithms and multi-armed bandit (MAB) algorithms perform optimization by repeatedly taking actions and observing the corresponding instant rewards without any prior knowledge. Recently, a BBO method using an Ising machine has been proposed to find the best action that is represented by a combination of discrete values and maximizes the instant reward in static environments. In contrast, dynamic environments, where real-time systems operate, necessitate MAB algorithms that maximize the average reward over multiple trials. However, due to the enormous number of actions resulting from the combinatorial nature of discrete optimization, conventional MAB algorithms cannot effectively optimize dynamic, discrete environments. Here, we show a heuristic MAB method for dynamic, discrete environments by extending the BBO method, in which an Ising machine effectively explores the actions while considering interactions between variables and changes in dynamic environments. We demonstrate the dynamic adaptability of the proposed method in a wireless communication system with moving users.
BIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarios
Authors: Liyang Yu, Tianyi Wang, Junfeng Jiao, Fengwu Shan, Hongqing Chu, Bingzhao Gao
Date: 2025-06-19 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Deep Reinforcement Learning

In complex real-world traffic environments, autonomous vehicles (AVs) need to interact with other traffic participants while making real-time and safety-critical decisions accordingly. The unpredictability of human behaviors poses significant challenges, particularly in dynamic scenarios, such as multi-lane highways and unsignalized T-intersections. To address this gap, we design a bi-level interaction decision-making algorithm (BIDA) that integrates interactive Monte Carlo tree search (MCTS) with deep reinforcement learning (DRL), aiming to enhance interaction rationality, efficiency and safety of AVs in dynamic key traffic scenarios. Specifically, we adopt three types of DRL algorithms to construct a reliable value network and policy network, which guide the online deduction process of interactive MCTS by assisting in value update and node selection. Then, a dynamic trajectory planner and a trajectory tracking controller are designed and implemented in CARLA to ensure smooth execution of planned maneuvers. Experimental evaluations demonstrate that our BIDA not only enhances interactive deduction and reduces computational costs, but also outperforms other latest benchmarks, which exhibits superior safety, efficiency and interaction rationality under varying traffic conditions.
Artificial Intelligence for Atmospheric Sciences: A Research Roadmap
Authors: Martha Arbayani Zaidan, Naser Hossein Motlagh, Petteri Nurmi, Tareq Hussein, Markku Kulmala, Tuukka Petäjä, Sasu Tarkoma
Date: 2025-06-19 00:00:00

Atmospheric sciences are crucial for understanding environmental phenomena ranging from air quality to extreme weather events, and climate change. Recent breakthroughs in sensing, communication, computing, and Artificial Intelligence (AI) have significantly advanced atmospheric sciences, enabling the generation of vast amounts of data through long-term Earth observations and providing powerful tools for analyzing atmospheric phenomena and predicting natural disasters. This paper contributes a critical interdisciplinary overview that bridges the fields of atmospheric science and computer science, highlighting the transformative potential of AI in atmospheric research. We identify key challenges associated with integrating AI into atmospheric research, including issues related to big data and infrastructure, and provide a detailed research roadmap that addresses both current and emerging challenges.
Quantum Artificial Intelligence for Secure Autonomous Vehicle Navigation: An Architectural Proposal
Authors: Hemanth Kannamarlapudi, Sowmya Chintalapudi
Date: 2025-06-19 00:00:00
Tasks: Autonomous Vehicles, Sensor Fusion

Navigation is a very crucial aspect of autonomous vehicle ecosystem which heavily relies on collecting and processing large amounts of data in various states and taking a confident and safe decision to define the next vehicle maneuver. In this paper, we propose a novel architecture based on Quantum Artificial Intelligence by enabling quantum and AI at various levels of navigation decision making and communication process in Autonomous vehicles : Quantum Neural Networks for multimodal sensor fusion, Nav-Q for Quantum reinforcement learning for navigation policy optimization and finally post-quantum cryptographic protocols for secure communication. Quantum neural networks uses quantum amplitude encoding to fuse data from various sensors like LiDAR, radar, camera, GPS and weather etc., This approach gives a unified quantum state representation between heterogeneous sensor modalities. Nav-Q module processes the fused quantum states through variational quantum circuits to learn optimal navigation policies under swift dynamic and complex conditions. Finally, post quantum cryptographic protocols are used to secure communication channels for both within vehicle communication and V2X (Vehicle to Everything) communications and thus secures the autonomous vehicle communication from both classical and quantum security threats. Thus, the proposed framework addresses fundamental challenges in autonomous vehicles navigation by providing quantum performance and future proof security. Index Terms Quantum Computing, Autonomous Vehicles, Sensor Fusion
Optimal Operating Strategy for PV-BESS Households: Balancing Self-Consumption and Self-Sufficiency
Authors: Jun Wook Heo, Raja Jurdak, Sara Khalifa
Date: 2025-06-10 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning (RL), Scheduling

High penetration of Photovoltaic (PV) generation and Battery Energy Storage System (BESS) in individual households increases the demand for solutions to determine the optimal PV generation power and the capacity of BESS. Self-consumption and self-sufficiency are essential for optimising the operation of PV-BESS systems in households, aiming to minimise power import from and export to the main grid. However, self-consumption and self-sufficiency are not independent; they share a linear relationship. This paper demonstrates this relationship and proposes an optimal operating strategy that considers power generation and consumption profiles to maximise self-consumption and self-sufficiency in households equipped with a PV-BESS. We classify self-consumption and self-sufficiency patterns into four categories based on the ratio of self-sufficiency to self-consumption for each household and determine the optimal PV generation and BESS capacities using both a mathematical calculation and this ratio. These optimal operation values for each category are then simulated using Model Predictive Control (MPC) and Reinforcement Learning (RL)-based battery charging and discharging scheduling models. The results show that the ratio between self-consumption and self-sufficiency is a useful metric for determining the optimal capacity of PV-BESS systems to maximise the local utilisation of PV-generated power.
Tutorial: $\varphi$-Transductions in OpenFst via the Gallic Semiring
Authors: Marco Cognetta, Cyril Allauzen
Date: 2025-06-22 00:00:00

OpenFst, a popular finite-state transducer library, supports $\varphi$-transitions but, due to an implementation constraint, they cannot be used with transducers in a straightforward way. In this short tutorial, we describe how one can use other functionality provided by OpenFst (namely, the Gallic semiring) to correctly implement $\varphi$-transductions and demonstrate it by implementing the MaxMatch (WordPiece) tokenization algorithm (Devlin et al., 2019; Song et al., 2021). Accompanying self-contained code examples are provided. https://www.openfst.org/twiki/pub/Contrib/FstContrib/phi_transduction_tutorial_code.tgz
Knee-Deep in C-RASP: A Transformer Depth Hierarchy
Authors: Andy Yang, Michaël Cadilhac, David Chiang
Date: 2025-06-19 00:00:00

It has been observed that transformers with greater depth (that is, more layers) have more capabilities, but can we establish formally which capabilities are gained with greater depth? We answer this question with a theoretical proof followed by an empirical study. First, we consider transformers that round to fixed precision except inside attention. We show that this subclass of transformers is expressively equivalent to the programming language C-RASP and this equivalence preserves depth. Second, we prove that deeper C-RASP programs are more expressive than shallower C-RASP programs, implying that deeper transformers are more expressive than shallower transformers (within the subclass mentioned above). These results are established by studying a form of temporal logic with counting operators, which was shown equivalent to C-RASP in previous work. Finally, we provide empirical evidence that our theory predicts the depth required for transformers without positional encodings to length-generalize on a family of sequential dependency tasks.
FairyGen: Storied Cartoon Video from a Single Child-Drawn Character
Authors: Jiayi Zheng, Xiaodong Cun
Date: 2025-06-26 00:00:00

We propose FairyGen, an automatic system for generating story-driven cartoon videos from a single child's drawing, while faithfully preserving its unique artistic style. Unlike previous storytelling methods that primarily focus on character consistency and basic motion, FairyGen explicitly disentangles character modeling from stylized background generation and incorporates cinematic shot design to support expressive and coherent storytelling. Given a single character sketch, we first employ an MLLM to generate a structured storyboard with shot-level descriptions that specify environment settings, character actions, and camera perspectives. To ensure visual consistency, we introduce a style propagation adapter that captures the character's visual style and applies it to the background, faithfully retaining the character's full visual identity while synthesizing style-consistent scenes. A shot design module further enhances visual diversity and cinematic quality through frame cropping and multi-view synthesis based on the storyboard. To animate the story, we reconstruct a 3D proxy of the character to derive physically plausible motion sequences, which are then used to fine-tune an MMDiT-based image-to-video diffusion model. We further propose a two-stage motion customization adapter: the first stage learns appearance features from temporally unordered frames, disentangling identity from motion; the second stage models temporal dynamics using a timestep-shift strategy with frozen identity weights. Once trained, FairyGen directly renders diverse and coherent video scenes aligned with the storyboard. Extensive experiments demonstrate that our system produces animations that are stylistically faithful, narratively structured natural motion, highlighting its potential for personalized and engaging story animation. The code will be available at https://github.com/GVCLab/FairyGen
BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing
Authors: Jiacheng Chen, Ramin Mehran, Xuhui Jia, Saining Xie, Sanghyun Woo
Date: 2025-06-20 00:00:00

We present BlenderFusion, a generative visual compositing framework that synthesizes new scenes by recomposing objects, camera, and background. It follows a layering-editing-compositing pipeline: (i) segmenting and converting visual inputs into editable 3D entities (layering), (ii) editing them in Blender with 3D-grounded control (editing), and (iii) fusing them into a coherent scene using a generative compositor (compositing). Our generative compositor extends a pre-trained diffusion model to process both the original (source) and edited (target) scenes in parallel. It is fine-tuned on video frames with two key training strategies: (i) source masking, enabling flexible modifications like background replacement; (ii) simulated object jittering, facilitating disentangled control over objects and camera. BlenderFusion significantly outperforms prior methods in complex compositional scene editing tasks.
Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models
Authors: Donggoo Kang, Jangyeong Kim, Dasol Jeong, Junyoung Choi, Jeonga Wi, Hyunmin Lee, Joonho Gwon, Joonki Paik
Date: 2025-06-26 00:00:00
Tasks: Texture Synthesis, Video Generation

Current texture synthesis methods, which generate textures from fixed viewpoints, suffer from inconsistencies due to the lack of global context and geometric understanding. Meanwhile, recent advancements in video generation models have demonstrated remarkable success in achieving temporally consistent videos. In this paper, we introduce VideoTex, a novel framework for seamless texture synthesis that leverages video generation models to address both spatial and temporal inconsistencies in 3D textures. Our approach incorporates geometry-aware conditions, enabling precise utilization of 3D mesh structures. Additionally, we propose a structure-wise UV diffusion strategy, which enhances the generation of occluded areas by preserving semantic information, resulting in smoother and more coherent textures. VideoTex not only achieves smoother transitions across UV boundaries but also ensures high-quality, temporally stable textures across video frames. Extensive experiments demonstrate that VideoTex outperforms existing methods in texture fidelity, seam blending, and stability, paving the way for dynamic real-time applications that demand both visual quality and temporal coherence.
3DGH: 3D Head Generation with Composable Hair and Face
Authors: Chengan He, Junxuan Li, Tobias Kirschstein, Artem Sevastopolsky, Shunsuke Saito, Qingyang Tan, Javier Romero, Chen Cao, Holly Rushmeier, Giljoo Nam
Date: 2025-06-25 00:00:00
Tasks: Image Generation

We present 3DGH, an unconditional generative model for 3D human heads with composable hair and face components. Unlike previous work that entangles the modeling of hair and face, we propose to separate them using a novel data representation with template-based 3D Gaussian Splatting, in which deformable hair geometry is introduced to capture the geometric variations across different hairstyles. Based on this data representation, we design a 3D GAN-based architecture with dual generators and employ a cross-attention mechanism to model the inherent correlation between hair and face. The model is trained on synthetic renderings using carefully designed objectives to stabilize training and facilitate hair-face separation. We conduct extensive experiments to validate the design choice of 3DGH, and evaluate it both qualitatively and quantitatively by comparing with several state-of-the-art 3D GAN methods, demonstrating its effectiveness in unconditional full-head image synthesis and composable 3D hairstyle editing. More details will be available on our project page: https://c-he.github.io/projects/3dgh/.