pwc-archive/papers-with-abstracts huggingface dataset:
Deep Generative Model-Based Generation of Synthetic Individual-Specific Brain MRI Segmentations
Authors: Ruijie Wang, Luca Rossetto, Susan Mérillat, Christina Röcke, Mike Martin, Abraham Bernstein
Date: 2025-04-15 00:00:00
Tasks: Generative Adversarial Network

To the best of our knowledge, all existing methods that can generate synthetic brain magnetic resonance imaging (MRI) scans for a specific individual require detailed structural or volumetric information about the individual's brain. However, such brain information is often scarce, expensive, and difficult to obtain. In this paper, we propose the first approach capable of generating synthetic brain MRI segmentations -- specifically, 3D white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) segmentations -- for individuals using their easily obtainable and often readily available demographic, interview, and cognitive test information. Our approach features a novel deep generative model, CSegSynth, which outperforms existing prominent generative models, including conditional variational autoencoder (C-VAE), conditional generative adversarial network (C-GAN), and conditional latent diffusion model (C-LDM). We demonstrate the high quality of our synthetic segmentations through extensive evaluations. Also, in assessing the effectiveness of the individual-specific generation, we achieve superior volume prediction, with mean absolute errors of only 36.44mL, 29.20mL, and 35.51mL between the ground-truth WM, GM, and CSF volumes of test individuals and those volumes predicted based on generated individual-specific segmentations, respectively.
3D Object Reconstruction with mmWave Radars
Authors: Samah Hussein, Junfeng Guan, Swathi Narashiman, Saurabh Gupta, Haitham Hassanieh
Date: 2025-04-15 00:00:00
Tasks: 3D Object Reconstruction, 3D Shape Reconstruction, Decoder, Object, Object Reconstruction

This paper presents RFconstruct, a framework that enables 3D shape reconstruction using commercial off-the-shelf (COTS) mmWave radars for self-driving scenarios. RFconstruct overcomes radar limitations of low angular resolution, specularity, and sparsity in radar point clouds through a holistic system design that addresses hardware, data processing, and machine learning challenges. The first step is fusing data captured by two radar devices that image orthogonal planes, then performing odometry-aware temporal fusion to generate denser 3D point clouds. RFconstruct then reconstructs 3D shapes of objects using a customized encoder-decoder model that does not require prior knowledge of the object's bound box. The shape reconstruction performance of RFconstruct is compared against 3D models extracted from a depth camera equipped with a LiDAR. We show that RFconstruct can accurately generate 3D shapes of cars, bikes, and pedestrians.
ConvShareViT: Enhancing Vision Transformers with Convolutional Attention Mechanisms for Free-Space Optical Accelerators
Authors: Riad Ibadulla, Thomas M. Chen, Constantino Carlos Reyes-Aldasoro
Date: 2025-04-15 00:00:00
Tasks: GPU

This paper introduces ConvShareViT, a novel deep learning architecture that adapts Vision Transformers (ViTs) to the 4f free-space optical system. ConvShareViT replaces linear layers in multi-head self-attention (MHSA) and Multilayer Perceptrons (MLPs) with a depthwise convolutional layer with shared weights across input channels. Through the development of ConvShareViT, the behaviour of convolutions within MHSA and their effectiveness in learning the attention mechanism were analysed systematically. Experimental results demonstrate that certain configurations, particularly those using valid-padded shared convolutions, can successfully learn attention, achieving comparable attention scores to those obtained with standard ViTs. However, other configurations, such as those using same-padded convolutions, show limitations in attention learning and operate like regular CNNs rather than transformer models. ConvShareViT architectures are specifically optimised for the 4f optical system, which takes advantage of the parallelism and high-resolution capabilities of optical systems. Results demonstrate that ConvShareViT can theoretically achieve up to 3.04 times faster inference than GPU-based systems. This potential acceleration makes ConvShareViT an attractive candidate for future optical deep learning applications and proves that our ViT (ConvShareViT) can be employed using only the convolution operation, via the necessary optimisation of the ViT to balance performance and complexity.
Learned enclosure method for experimental EIT data
Authors: Sara Sippola, Siiri Rautio, Andreas Hauptmann, Takanori Ide, Samuli Siltanen
Date: 2025-04-15 00:00:00

Electrical impedance tomography (EIT) is a non-invasive imaging method with diverse applications, including medical imaging and non-destructive testing. The inverse problem of reconstructing internal electrical conductivity from boundary measurements is nonlinear and highly ill-posed, making it difficult to solve accurately. In recent years, there has been growing interest in combining analytical methods with machine learning to solve inverse problems. In this paper, we propose a method for estimating the convex hull of inclusions from boundary measurements by combining the enclosure method proposed by Ikehata with neural networks. We demonstrate its performance using experimental data. Compared to the classical enclosure method with least squares fitting, the learned convex hull achieves superior performance on both simulated and experimental data.
TransitReID: Transit OD Data Collection with Occlusion-Resistant Dynamic Passenger Re-Identification
Authors: Kaicong Huang, Talha Azfar, Jack Reilly, Ruimin Ke
Date: 2025-04-15 00:00:00
Tasks: Person Re-Identification

Transit Origin-Destination (OD) data are essential for transit planning, particularly in route optimization and demand-responsive paratransit systems. Traditional methods, such as manual surveys, are costly and inefficient, while Bluetooth and WiFi-based approaches require passengers to carry specific devices, limiting data coverage. On the other hand, most transit vehicles are equipped with onboard cameras for surveillance, offering an opportunity to repurpose them for edge-based OD data collection through visual person re-identification (ReID). However, such approaches face significant challenges, including severe occlusion and viewpoint variations in transit environments, which greatly reduce matching accuracy and hinder their adoption. Moreover, designing effective algorithms that can operate efficiently on edge devices remains an open challenge. To address these challenges, we propose TransitReID, a novel framework for individual-level transit OD data collection. TransitReID consists of two key components: (1) An occlusion-robust ReID algorithm featuring a variational autoencoder guided region-attention mechanism that adaptively focuses on visible body regions through reconstruction loss-optimized weight allocation; and (2) a Hierarchical Storage and Dynamic Matching (HSDM) mechanism specifically designed for efficient and robust transit OD matching which balances storage, speed, and accuracy. Additionally, a multi-threaded design supports near real-time operation on edge devices, which also ensuring privacy protection. We also introduce a ReID dataset tailored for complex bus environments to address the lack of relevant training data. Experimental results demonstrate that TransitReID achieves state-of-the-art performance in ReID tasks, with an accuracy of approximately 90\% in bus route simulations.
Deciphering scrolls with tomography: A training experiment
Authors: Sonia Foschiatti, Axel Kittenberger, Otmar Scherzer
Date: 2025-04-14 00:00:00
Tasks: Computed Tomography (CT)

The recovery of severely damaged ancient written documents has proven to be a major challenge for many scientists, mainly due to the impracticality of physical unwrapping them. Non-destructive techniques, such as X-ray computed tomography (CT), combined with computer vision algorithms, have emerged as a means of facilitating the virtual reading of the hidden contents of the damaged documents. This paper proposes an educational laboratory aimed at simulating the entire process of acquisition and virtual recovery of the ancient works. We have developed an experimental setup that uses visible light to replace the detrimental X-rays, and a didactic software pipeline that allows students to virtually reconstruct a transparent rolled sheet with printed text on it, the wrapped scroll.
Local Temporal Feature Enhanced Transformer with ROI-rank Based Masking for Diagnosis of ADHD
Authors: Byunggun Kim, Younghun Kwon
Date: 2025-04-12 00:00:00

In modern society, Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the common mental diseases discovered not only in children but also in adults. In this context, we propose a ADHD diagnosis transformer model that can effectively simultaneously find important brain spatiotemporal biomarkers from resting-state functional magnetic resonance (rs-fMRI). This model not only learns spatiotemporal individual features but also learns the correlation with full attention structures specialized in ADHD diagnosis. In particular, it focuses on learning local blood oxygenation level dependent (BOLD) signals and distinguishing important regions of interest (ROI) in the brain. Specifically, the three proposed methods for ADHD diagnosis transformer are as follows. First, we design a CNN-based embedding block to obtain more expressive embedding features in brain region attention. It is reconstructed based on the previously CNN-based ADHD diagnosis models for the transformer. Next, for individual spatiotemporal feature attention, we change the attention method to local temporal attention and ROI-rank based masking. For the temporal features of fMRI, the local temporal attention enables to learn local BOLD signal features with only simple window masking. For the spatial feature of fMRI, ROI-rank based masking can distinguish ROIs with high correlation in ROI relationships based on attention scores, thereby providing a more specific biomarker for ADHD diagnosis. The experiment was conducted with various types of transformer models. To evaluate these models, we collected the data from 939 individuals from all sites provided by the ADHD-200 competition. Through this, the spatiotemporal enhanced transformer for ADHD diagnosis outperforms the performance of other different types of transformer variants. (77.78ACC 76.60SPE 79.22SEN 79.30AUC)
High Dynamic Range Modulo Imaging for Robust Object Detection in Autonomous Driving
Authors: Kebin Contreras, Brayan Monroy, Jorge Bacca
Date: 2025-04-11 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Image Reconstruction, object-detection, Object Detection, Robust Object Detection

Object detection precision is crucial for ensuring the safety and efficacy of autonomous driving systems. The quality of acquired images directly influences the ability of autonomous driving systems to correctly recognize and respond to other vehicles, pedestrians, and obstacles in real-time. However, real environments present extreme variations in lighting, causing saturation problems and resulting in the loss of crucial details for detection. Traditionally, High Dynamic Range (HDR) images have been preferred for their ability to capture a broad spectrum of light intensities, but the need for multiple captures to construct HDR images is inefficient for real-time applications in autonomous vehicles. To address these issues, this work introduces the use of modulo sensors for robust object detection. The modulo sensor allows pixels to `reset/wrap' upon reaching saturation level by acquiring an irradiance encoding image which can then be recovered using unwrapping algorithms. The applied reconstruction techniques enable HDR recovery of color intensity and image details, ensuring better visual quality even under extreme lighting conditions at the cost of extra time. Experiments with the YOLOv10 model demonstrate that images processed using modulo images achieve performance comparable to HDR images and significantly surpass saturated images in terms of object detection accuracy. Moreover, the proposed modulo imaging step combined with HDR image reconstruction is shorter than the time required for conventional HDR image acquisition.
Do Segmentation Models Understand Vascular Structure? A Blob-Based XAI Framework
Authors: Guillaume Garret, Antoine Vacavant, Carole Frindel
Date: 2025-04-11 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Deep learning models have achieved impressive performance in medical image segmentation, yet their black-box nature limits clinical adoption. In vascular applications, trustworthy segmentation should rely on both local image cues and global anatomical structures, such as vessel connectivity or branching. However, the extent to which models leverage such global context remains unclear. We present a novel explainability pipeline for 3D vessel segmentation, combining gradient-based attribution with graph-guided point selection and a blob-based analysis of Saliency maps. Using vascular graphs extracted from ground truth, we define anatomically meaningful points of interest (POIs) and assess the contribution of input voxels via Saliency maps. These are analyzed at both global and local scales using a custom blob detector. Applied to IRCAD and Bullitt datasets, our analysis shows that model decisions are dominated by highly localized attribution blobs centered near POIs. Attribution features show little correlation with vessel-level properties such as thickness, tubularity, or connectivity -- suggesting limited use of global anatomical reasoning. Our results underline the importance of structured explainability tools and highlight the current limitations of segmentation models in capturing global vascular context.
MultiCore+TPU Accelerated Multi-Modal TinyML for Livestock Behaviour Recognition
Authors: Qianxue Zhang, Eiman Kanjo
Date: 2025-04-10 00:00:00
Tasks: Activity Recognition, image-classification, Image Classification, object-detection, Object Detection

The advancement of technology has revolutionised the agricultural industry, transitioning it from labour-intensive farming practices to automated, AI-powered management systems. In recent years, more intelligent livestock monitoring solutions have been proposed to enhance farming efficiency and productivity. This work presents a novel approach to animal activity recognition and movement tracking, leveraging tiny machine learning (TinyML) techniques, wireless communication framework, and microcontroller platforms to develop an efficient, cost-effective livestock sensing system. It collects and fuses accelerometer data and vision inputs to build a multi-modal network for three tasks: image classification, object detection, and behaviour recognition. The system is deployed and evaluated on commercial microcontrollers for real-time inference using embedded applications, demonstrating up to 270$\times$ model size reduction, less than 80ms response latency, and on-par performance comparable to existing methods. The incorporation of the TinyML technique allows for seamless data transmission between devices, benefiting use cases in remote locations with poor Internet connectivity. This work delivers a robust, scalable IoT-edge livestock monitoring solution adaptable to diverse farming needs, offering flexibility for future extensions.
A quantum information-based refoundation of color perception concepts
Authors: Michel Berthier, Nicoletta Prencipe, Edoardo Provenzi
Date: 2025-03-29 00:00:00

In this paper we deal with the problem of overcoming the intuitive definition of several color perception attributes by replacing them with novel mathematically rigorous ones. Our framework is a quantum-like color perception theory recently developed, which constitutes a radical change of view with respect to the classical CIE models and their color appearance counterparts. We show how quantum information concepts, as e.g. effects, generalized states, post-measurement transformations and relative entropy provide tools that seem to be perfectly fit to model color perception attributes as brightness, lightness, colorfulness, chroma, saturation and hue. An illustration of the efficiency of these novel definitions is provided by the rigorous derivation of the so-called lightness constancy phenomenon.
Quantum measurement and color perception: theory and applications
Authors: Michel Berthier, Edoardo Provenzi
Date: 2025-03-29 00:00:00

In this paper we make a systematic use of the quantum measurement theory to describe perceived colors and analyze some of their fundamental properties. After motivating the naturalness of the quantum measurement approach in the mathematical framework of the color perception theory proposed by the authors in previous papers, we show how to obtain several results. Among our theoretical outcomes, we mention the possibility to confine the color cone to a finite-volume color solid and the link between post-measurement state changes, Lorentz boosts and the Einstein-Poincar\'e relativistic addition law. We apply these results to obtain a chromatic match equation that emphasizes the importance of the Hilbert-Klein metric on the unit disk and we also present a quantitative description of Hunt's effect.
The quantum nature of color perception: Uncertainty relations for chromatic opposition
Authors: Michel Berthier, Edoardo Provenzi
Date: 2025-03-28 00:00:00

In this paper we provide an overview on the foundation and first results of a very recent quantum theory of color perception, together with novel results about uncertainty relations for chromatic opposition. The major inspiration for this model is the 1974 remarkable work by H.L. Resnikoff, who had the idea to give up the analysis of the space of perceived colors through metameric classes of spectra in favor of the study of its algebraic properties. This strategy permitted to reveal the importance of hyperbolic geometry in colorimetry. Starting from these premises, we show how Resnikoff's construction can be extended to a geometrically rich quantum framework, where the concepts of achromatic color, hue and saturation can be rigorously defined. Moreover, the analysis of pure and mixed quantum chromatic states leads to a deep understanding of chromatic opposition and its role in the encoding of visual signals. We complete our paper by proving the existence of uncertainty relations for the degree of chromatic opposition, thus providing a theoretical confirmation of the quantum nature of color perception.
ORIS allocation to minimize the outage probability in a multi-user VLC scenario
Authors: Borja Genoves Guzman, Maïté Brandt-Pearce
Date: 2025-04-17 00:00:00

Visible Light Communication (VLC) is a promising solution to address the growing demand for wireless data, leveraging the widespread use of light-emitting diodes (LEDs) as transmitters. However, its deployment is challenged by link blockages that cause connectivity outages. Optical reconfigurable intelligent surfaces (ORISs) have recently emerged as a solution to mitigate these disruptions. This work considers a multi-user VLC system and investigates the optimal association of ORISs to LEDs and users to minimize the outage probability while limiting the number of ORISs used. Numerical results from our proposed optimization algorithm demonstrate that using ORISs can reduce the outage probability by up to 85% compared to a no-ORIS scenario.
Simultaneous Polysomnography and Cardiotocography Reveal Temporal Correlation Between Maternal Obstructive Sleep Apnea and Fetal Hypoxia
Authors: Jingyu Wang, Donglin Xie, Jingying Ma, Yunliang Sun, Linyan Zhang, Rui Bai, Zelin Tu, Liyue Xu, Jun Wei, Jingjing Yang, Yanan Liu, Huijie Yi, Bing Zhou, Long Zhao, Xueli Zhang, Mengling Feng, Xiaosong Dong, Guoli Liu, Fang Han, Shenda Hong
Date: 2025-04-17 00:00:00

Background: Obstructive sleep apnea syndrome (OSAS) during pregnancy is common and can negatively affect fetal outcomes. However, studies on the immediate effects of maternal hypoxia on fetal heart rate (FHR) changes are lacking. Methods: We used time-synchronized polysomnography (PSG) and cardiotocography (CTG) data from two cohorts to analyze the correlation between maternal hypoxia and FHR changes (accelerations or decelerations). Maternal hypoxic event characteristics were analyzed using generalized linear modeling (GLM) to assess their associations with different FHR changes. Results: A total of 118 pregnant women participated. FHR changes were significantly associated with maternal hypoxia, primarily characterized by accelerations. A longer hypoxic duration correlated with more significant FHR accelerations (P < 0.05), while prolonged hypoxia and greater SpO2 drop were linked to FHR decelerations (P < 0.05). Both cohorts showed a transient increase in FHR during maternal hypoxia, which returned to baseline after the event resolved. Conclusion: Maternal hypoxia significantly affects FHR, suggesting that maternal OSAS may contribute to fetal hypoxia. These findings highlight the importance of maternal-fetal interactions and provide insights for future interventions.
Optic Fingerprint(OFP): Enhancing Security in Li-Fi Networks
Authors: Ziqi Liu, Xuanbang Chen, Xun Zhang
Date: 2025-04-17 00:00:00

We present a hardware-integrated security framework for LiFi networks through device fingerprint extraction within the IEEE 802.15.7 protocol. Our Optic Fingerprint (OFP) model utilizes inherent LED nonlinearities to generate amplitude-based feature vectors in time and frequency domains, specifically designed for optical wireless systems. Experimental results with 39 commercial LEDs demonstrate 90.36% classification accuracy across SNR 10-30 dB while maintaining standard compliance, offering a practical physical-layer authentication solution for visible light communication.
Supporting Urban Low-Altitude Economy: Channel Gain Map Inference Based on 3D Conditional GAN
Authors: Yonghao Wang, Ruoguang Li, Di wu, Jiaqi Chen, Yong Zeng
Date: 2025-04-17 00:00:00
Tasks: Generative Adversarial Network

The advancement of advanced air mobility (AAM) in recent years has given rise to the concept of low-altitude economy (LAE). However, the diverse flight activities associated with the emerging LAE applications in urban scenarios confront complex physical environments, which urgently necessitates ubiquitous and reliable communication to guarantee the operation safety of the low-altitude aircraft. As one of promising technologies for the sixth generation (6G) mobile networks, channel knowledge map (CKM) enables the environment-aware communication by constructing a site-specific dataset, thereby providing a priori on-site information for the aircraft to obtain the channel state information (CSI) at arbitrary locations with much reduced online overhead. Diverse base station (BS) deployments in the three-dimensional (3D) urban low-altitude environment require efficient 3D CKM construction to capture spatial channel characteristics with less overhead. Towards this end, this paper proposes a 3D channel gain map (CGM) inference method based on a 3D conditional generative adversarial network (3D-CGAN). Specifically, we first analyze the potential deployment types of BSs in urban low-altitude scenario, and investigate the CGM representation with the corresponding 3D channel gain model. The framework of the proposed 3D-CGAN is then discussed, which is trained by a dataset consisting of existing CGMs. Consequently, the trained 3D-CGAN is capable of inferring the corresponding CGM only based on the BS coordinate without additional measurement. The simulation results demonstrate that the CGMs inferred by the proposed 3D-CGAN outperform those of the benchmark schemes, which can accurately reflect the radio propagation condition in 3D environment.