pwc-archive/papers-with-abstracts huggingface dataset:
Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction
Authors: Simon Giebenhain, Tobias Kirschstein, Martin Rünz, Lourdes Agapito, Matthias Nießner
Date: 2025-05-01 00:00:00
Tasks: 3D Face Reconstruction, 3D Reconstruction, Diversity, Face Model, Face Reconstruction

We address the 3D reconstruction of human faces from a single RGB image. To this end, we propose Pixel3DMM, a set of highly-generalized vision transformers which predict per-pixel geometric cues in order to constrain the optimization of a 3D morphable face model (3DMM). We exploit the latent features of the DINO foundation model, and introduce a tailored surface normal and uv-coordinate prediction head. We train our model by registering three high-quality 3D face datasets against the FLAME mesh topology, which results in a total of over 1,000 identities and 976K images. For 3D face reconstruction, we propose a FLAME fitting opitmization that solves for the 3DMM parameters from the uv-coordinate and normal estimates. To evaluate our method, we introduce a new benchmark for single-image face reconstruction, which features high diversity facial expressions, viewing angles, and ethnicities. Crucially, our benchmark is the first to evaluate both posed and neutral facial geometry. Ultimately, our method outperforms the most competitive baselines by over 15% in terms of geometric accuracy for posed facial expressions.
Dietary Intake Estimation via Continuous 3D Reconstruction of Food
Authors: Wallace Lee, Yuhao Chen
Date: 2025-05-01 00:00:00
Tasks: 3D Reconstruction, Pose Estimation

Monitoring dietary habits is crucial for preventing health risks associated with overeating and undereating, including obesity, diabetes, and cardiovascular diseases. Traditional methods for tracking food intake rely on self-reported data before or after the eating, which are prone to inaccuracies. This study proposes an approach to accurately monitor ingest behaviours by leveraging 3D food models constructed from monocular 2D video. Using COLMAP and pose estimation algorithms, we generate detailed 3D representations of food, allowing us to observe changes in food volume as it is consumed. Experiments with toy models and real food items demonstrate the approach's potential. Meanwhile, we have proposed a new methodology for automated state recognition challenges to accurately detect state changes and maintain model fidelity. The 3D reconstruction approach shows promise in capturing comprehensive dietary behaviour insights, ultimately contributing to the development of automated and accurate dietary monitoring tools.
Visual Trajectory Prediction of Vessels for Inland Navigation
Authors: Alexander Puzicha, Konstantin Wüstefeld, Kathrin Wilms, Frank Weichert
Date: 2025-05-01 00:00:00
Tasks: Collision Avoidance, object-detection, Object Detection, Prediction, Trajectory Prediction

The future of inland navigation increasingly relies on autonomous systems and remote operations, emphasizing the need for accurate vessel trajectory prediction. This study addresses the challenges of video-based vessel tracking and prediction by integrating advanced object detection methods, Kalman filters, and spline-based interpolation. However, existing detection systems often misclassify objects in inland waterways due to complex surroundings. A comparative evaluation of tracking algorithms, including BoT-SORT, Deep OC-SORT, and ByeTrack, highlights the robustness of the Kalman filter in providing smoothed trajectories. Experimental results from diverse scenarios demonstrate improved accuracy in predicting vessel movements, which is essential for collision avoidance and situational awareness. The findings underline the necessity of customized datasets and models for inland navigation. Future work will expand the datasets and incorporate vessel classification to refine predictions, supporting both autonomous systems and human operators in complex environments.
Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime
Authors: Francesco Camilli, Daria Tieplova, Eleonora Bergamin, Jean Barbier
Date: 2025-05-06 00:00:00

We rigorously analyse fully-trained neural networks of arbitrary depth in the Bayesian optimal setting in the so-called proportional scaling regime where the number of training samples and width of the input and all inner layers diverge proportionally. We prove an information-theoretic equivalence between the Bayesian deep neural network model trained from data generated by a teacher with matching architecture, and a simpler model of optimal inference in a generalized linear model. This equivalence enables us to compute the optimal generalization error for deep neural networks in this regime. We thus prove the "deep Gaussian equivalence principle" conjectured in Cui et al. (2023) (arXiv:2302.00375). Our result highlights that in order to escape this "trivialisation" of deep neural networks (in the sense of reduction to a linear model) happening in the strongly overparametrized proportional regime, models trained from much more data have to be considered.
CB-cPIR: Code-Based Computational Private Information Retrieval
Authors: Camilla Hollanti, Neehar Verma
Date: 2025-05-06 00:00:00
Tasks: Information Retrieval, Retrieval

A private information retrieval (PIR) scheme is a protocol that allows a user to retrieve a file from a database without revealing the identity of the desired file to a curious database. Given a distributed data storage system, efficient PIR can be achieved by making assumptions about the colluding capabilities of the storage servers holding the database. If these assumptions turn out to be incorrect, privacy is lost. In this work, we focus on the worst-case assumption: full collusion or, equivalently, viewing the storage system virtually as a single honest-but-curious server. We present CB-cPIR, a single-server code-based computational private information retrieval (cPIR) scheme that derives security from code-based cryptography. Specifically, the queries are protected by the hardness of decoding a random linear code. The scheme is heavily inspired by the pioneering code-based cPIR scheme proposed by Holzbaur, Hollanti, and Wachter-Zeh in [Holzbaur et al., "Computational Code-Based Single-Server Private Information Retrieval", 2020 IEEE ISIT] and fixes the vulnerabilities of the original scheme arising from highly probable rank differences in submatrices of the user's query. For further validation, we draw comparisons to the state-of-the-art lattice-based cPIR schemes.
Uncertainty-Aware Multi-Expert Knowledge Distillation for Imbalanced Disease Grading
Authors: Shuo Tong, Shangde Gao, Ke Liu, Zihang Huang, Hongxia Xu, Haochao Ying, Jian Wu
Date: 2025-05-01 00:00:00
Tasks: Knowledge Distillation, Transfer Learning

Automatic disease image grading is a significant application of artificial intelligence for healthcare, enabling faster and more accurate patient assessments. However, domain shifts, which are exacerbated by data imbalance, introduce bias into the model, posing deployment difficulties in clinical applications. To address the problem, we propose a novel \textbf{U}ncertainty-aware \textbf{M}ulti-experts \textbf{K}nowledge \textbf{D}istillation (UMKD) framework to transfer knowledge from multiple expert models to a single student model. Specifically, to extract discriminative features, UMKD decouples task-agnostic and task-specific features with shallow and compact feature alignment in the feature space. At the output space, an uncertainty-aware decoupled distillation (UDD) mechanism dynamically adjusts knowledge transfer weights based on expert model uncertainties, ensuring robust and reliable distillation. Additionally, UMKD also tackles the problems of model architecture heterogeneity and distribution discrepancies between source and target domains, which are inadequately tackled by previous KD approaches. Extensive experiments on histology prostate grading (\textit{SICAPv2}) and fundus image grading (\textit{APTOS}) demonstrate that UMKD achieves a new state-of-the-art in both source-imbalanced and target-imbalanced scenarios, offering a robust and practical solution for real-world disease image grading.
GNN-enabled Precoding for Massive MIMO LEO Satellite Communications
Authors: Huibin Zhou, Xinrui Gong, Christos G. Tsinos, Li You, Xiqi Gao, Björn Ottersten
Date: 2025-05-06 00:00:00
Tasks: Computational Efficiency, Graph Neural Network

Low Earth Orbit (LEO) satellite communication is a critical component in the development of sixth generation (6G) networks. The integration of massive multiple-input multiple-output (MIMO) technology is being actively explored to enhance the performance of LEO satellite communications. However, the limited power of LEO satellites poses a significant challenge in improving communication energy efficiency (EE) under constrained power conditions. Artificial intelligence (AI) methods are increasingly recognized as promising solutions for optimizing energy consumption while enhancing system performance, thus enabling more efficient and sustainable communications. This paper proposes approaches to address the challenges associated with precoding in massive MIMO LEO satellite communications. First, we introduce an end-to-end graph neural network (GNN) framework that effectively reduces the computational complexity of traditional precoding methods. Next, we introduce a deep unfolding of the Dinkelbach algorithm and the weighted minimum mean square error (WMMSE) approach to achieve enhanced EE, transforming iterative optimization processes into a structured neural network, thereby improving convergence speed and computational efficiency. Furthermore, we incorporate the Taylor expansion method to approximate matrix inversion within the GNN, enhancing both the interpretability and performance of the proposed method. Numerical experiments demonstrate the validity of our proposed method in terms of complexity and robustness, achieving significant improvements over state-of-the-art methods.
Synthesizing and Identifying Noise Levels in Autonomous Vehicle Camera Radar Datasets
Authors: Mathis Morales, Golnaz Habibi
Date: 2025-05-01 00:00:00
Tasks: Autonomous Navigation, Data Augmentation, object-detection, Object Detection

Detecting and tracking objects is a crucial component of any autonomous navigation method. For the past decades, object detection has yielded promising results using neural networks on various datasets. While many methods focus on performance metrics, few projects focus on improving the robustness of these detection and tracking pipelines, notably to sensor failures. In this paper we attempt to address this issue by creating a realistic synthetic data augmentation pipeline for camera-radar Autonomous Vehicle (AV) datasets. Our goal is to accurately simulate sensor failures and data deterioration due to real-world interferences. We also present our results of a baseline lightweight Noise Recognition neural network trained and tested on our augmented dataset, reaching an overall recognition accuracy of 54.4\% on 11 categories across 10086 images and 2145 radar point-clouds.
Integrated Sensing, Computing, Communication, and Control for Time-Sequence-Based Semantic Communications
Authors: Qingliang Li, Bo Chang, Weidong Mei, Zhi Chen
Date: 2025-05-06 00:00:00
Tasks: Deep Reinforcement Learning, Semantic Communication

In the upcoming industrial internet of things (IIoT) era, a surge of task-oriented applications will rely on real-time wireless control systems (WCSs). For these systems, ultra-reliable and low-latency wireless communication will be crucial to ensure the timely transmission of control information. To achieve this purpose, we propose a novel time-sequence-based semantic communication paradigm, where an integrated sensing, computing, communication, and control (ISC3) architecture is developed to make sensible semantic inference (SI) for the control information over time sequences, enabling adaptive control of the robot. However, due to the causal correlations in the time sequence, the control information does not present the Markov property. To address this challenge, we compute the mutual information of the control information sensed at the transmitter (Tx) over different time and identify their temporal semantic correlation via a semantic feature extractor (SFE) module. By this means, highly correlated information transmission can be avoided, thus greatly reducing the communication overhead. Meanwhile, a semantic feature reconstructor (SFR) module is employed at the receiver (Rx) to reconstruct the control information based on the previously received one if the information transmission is not activated at the Tx. Furthermore, a control gain policy is also employed at the Rx to adaptively adjust the control gain for the controlled target based on several practical aspects such as the quality of the information transmission from the Tx to the Rx. We design the neural network structures of the above modules/policies and train their parameters by a novel hybrid reward multi-agent deep reinforcement learning framework. On-site experiments are conducted to evaluate the performance of our proposed method in practice, which shows significant gains over other baseline schemes.
X-ray illicit object detection using hybrid CNN-transformer neural network architectures
Authors: Jorgen Cani, Christos Diou, Spyridon Evangelatos, Panagiotis Radoglou-Grammatikis, Vasileios Argyriou, Panagiotis Sarigiannidis, Iraklis Varlamis, Georgios Th. Papadopoulos
Date: 2025-05-01 00:00:00
Tasks: object-detection, Object Detection

In the field of X-ray security applications, even the smallest details can significantly impact outcomes. Objects that are heavily occluded or intentionally concealed pose a great challenge for detection, whether by human observation or through advanced technological applications. While certain Deep Learning (DL) architectures demonstrate strong performance in processing local information, such as Convolutional Neural Networks (CNNs), others excel in handling distant information, e.g., transformers. In X-ray security imaging the literature has been dominated by the use of CNN-based methods, while the integration of the two aforementioned leading architectures has not been sufficiently explored. In this paper, various hybrid CNN-transformer architectures are evaluated against a common CNN object detection baseline, namely YOLOv8. In particular, a CNN (HGNetV2) and a hybrid CNN-transformer (Next-ViT-S) backbone are combined with different CNN/transformer detection heads (YOLOv8 and RT-DETR). The resulting architectures are comparatively evaluated on three challenging public X-ray inspection datasets, namely EDS, HiXray, and PIDray. Interestingly, while the YOLOv8 detector with its default backbone (CSP-DarkNet53) is generally shown to be advantageous on the HiXray and PIDray datasets, when a domain distribution shift is incorporated in the X-ray images (as happens in the EDS datasets), hybrid CNN-transformer architectures exhibit increased robustness. Detailed comparative evaluation results, including object-level detection performance and object-size error analysis, demonstrate the strengths and weaknesses of each architectural combination and suggest guidelines for future research. The source code and network weights of the models employed in this study are available at https://github.com/jgenc/xray-comparative-evaluation.
USF Spectral Estimation: Prevalence of Gaussian Cramér-Rao Bounds Despite Modulo Folding
Authors: Ruiming Guo, Ayush Bhandari
Date: 2025-05-06 00:00:00
Tasks: Super-Resolution

Spectral Estimation (SpecEst) is a core area of signal processing with a history spanning two centuries and applications across various fields. With the advent of digital acquisition, SpecEst algorithms have been widely applied to tasks like frequency super-resolution. However, conventional digital acquisition imposes a trade-off: for a fixed bit budget, one can optimize either signal dynamic range or digital resolution (noise floor), but not both simultaneously. The Unlimited Sensing Framework (USF) overcomes this limitation using modulo non-linearity in analog hardware, enabling a novel approach to SpecEst (USF-SpecEst). However, USF-SpecEst requires new theoretical and algorithmic developments to handle folded samples effectively. In this paper, we derive the Cram\'er-Rao Bounds (CRBs) for SpecEst with noisy modulo-folded samples and reveal a surprising result: the CRBs for USF-SpecEst are scaled versions of the Gaussian CRBs for conventional samples. Numerical experiments validate these bounds, providing a benchmark for USF-SpecEst and facilitating its practical deployment.
Energy Efficiency Maximization for CR-NOMA based Smart Grid Communication Network
Authors: Mubashar Sarfraz, Sheraz Alam, Sajjad A. Ghauri, Asad Mahmood
Date: 2025-05-05 00:00:00

Managing massive data flows effectively and resolving spectrum shortages are two challenges that Smart Grid Communication Networks (SGCN) must overcome. To address these problems, we provide a combined optimization approach that makes use of Cognitive Radio (CR) and Non-Orthogonal Multiple Access (NOMA) technologies. Our work focuses on using user pairing (UP) and power allocation (PA) techniques to maximize energy efficiency (EE) in SGCN, particularly within Neighbourhood Area Networks (NANs). We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting. This problem is NP-hard, nonlinear, and nonconvex by nature. To address the computational complexity of the problem, we use the Block Coordinate Descent (BCD) method, which breaks the problem into UP and PA subproblems. Initially, we proposed the Zebra-Optimization User Pairing (ZOUP) algorithm to tackle the UP problem, which outperforms both Orthogonal Multiple Access (OMA) and non-optimized NOMA (UPWO) by 78.8\% and 13.6\%, respectively, at a SNR of 15 dB. Based on the ZOUP pairs, we subsequently proposed the PA approach, i.e., ZOUPPA, which significantly outperforms UPWO and ZOUP by 53.2\% and 25.4\%, respectively, at an SNR of 15 dB. A detailed analysis of key parameters, including varying SNRs, power allocation constants, path loss exponents, user density, channel availability, and coverage radius, underscores the superiority of our approach. By facilitating the effective use of communication resources in SGCN, our research opens the door to more intelligent and energy-efficient grid systems. Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA.
A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic
Authors: Muhammad Imran Zaman, Usama Ijaz Bajwa, Gulshan Saleem, Rana Hammad Raza
Date: 2025-05-01 00:00:00
Tasks: object-detection, Object Detection, Object Tracking, Transfer Learning

Vision sensors are becoming more important in Intelligent Transportation Systems (ITS) for traffic monitoring, management, and optimization as the number of network cameras continues to rise. However, manual object tracking and matching across multiple non-overlapping cameras pose significant challenges in city-scale urban traffic scenarios. These challenges include handling diverse vehicle attributes, occlusions, illumination variations, shadows, and varying video resolutions. To address these issues, we propose an efficient and cost-effective deep learning-based framework for Multi-Object Multi-Camera Tracking (MO-MCT). The proposed framework utilizes Mask R-CNN for object detection and employs Non-Maximum Suppression (NMS) to select target objects from overlapping detections. Transfer learning is employed for re-identification, enabling the association and generation of vehicle tracklets across multiple cameras. Moreover, we leverage appropriate loss functions and distance measures to handle occlusion, illumination, and shadow challenges. The final solution identification module performs feature extraction using ResNet-152 coupled with Deep SORT based vehicle tracking. The proposed framework is evaluated on the 5th AI City Challenge dataset (Track 3), comprising 46 camera feeds. Among these 46 camera streams, 40 are used for model training and validation, while the remaining six are utilized for model testing. The proposed framework achieves competitive performance with an IDF1 score of 0.8289, and precision and recall scores of 0.9026 and 0.8527 respectively, demonstrating its effectiveness in robust and accurate vehicle tracking.
Design and Channel Modeling of Electromagnetically Reconfigurable Antennas
Authors: Ruiqi Wang, Pinjun Zheng, Tareq Y. Al-Naffouri, Atif Shamim
Date: 2025-05-04 00:00:00

In this work, a novel design of electromagnetically reconfigurable antennas (ERAs) based on a fluid antenna system (FAS) is proposed, and the corresponding wireless channel model is established. Different from conventional antenna arrays with static elements, the electromagnetic characteristics of each array element in the proposed ERA can be flexibly reconfigured into various states, introducing electromagnetic degrees of freedom to enhance wireless system performance. Based on the proposed ERA design, the corresponding channel model is developed. Finally, full-wave simulations are conducted to validate the overall design concept. The results reveal that a gain enhancement of 2.5 dB is achieved at a beamforming direction.
A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities
Authors: Abu Saleh Musa Miah, taro Suzuki, Jungpil Shin
Date: 2025-05-01 00:00:00
Tasks: Articles, EEG

Parkinsons Disease (PD) is a progressive neurological disorder that primarily affects motor functions and can lead to mild cognitive impairment (MCI) and dementia in its advanced stages. With approximately 10 million people diagnosed globally 1 to 1.8 per 1,000 individuals, according to reports by the Japan Times and the Parkinson Foundation early and accurate diagnosis of PD is crucial for improving patient outcomes. While numerous studies have utilized machine learning (ML) and deep learning (DL) techniques for PD recognition, existing surveys are limited in scope, often focusing on single data modalities and failing to capture the potential of multimodal approaches. To address these gaps, this study presents a comprehensive review of PD recognition systems across diverse data modalities, including Magnetic Resonance Imaging (MRI), gait-based pose analysis, gait sensory data, handwriting analysis, speech test data, Electroencephalography (EEG), and multimodal fusion techniques. Based on over 347 articles from leading scientific databases, this review examines key aspects such as data collection methods, settings, feature representations, and system performance, with a focus on recognition accuracy and robustness. This survey aims to serve as a comprehensive resource for researchers, providing actionable guidance for the development of next generation PD recognition systems. By leveraging diverse data modalities and cutting-edge machine learning paradigms, this work contributes to advancing the state of PD diagnostics and improving patient care through innovative, multimodal approaches.