pwc-archive/papers-with-abstracts huggingface dataset:
MetricGrids: Arbitrary Nonlinear Approximation with Elementary Metric Grids based Implicit Neural Representation
Authors: Shu Wang, Yanbo Gao, Shuai Li, Chong Lv, Xun Cai, Chuankun Li, Hui Yuan, Jinglin Zhang
Date: 2025-03-13 00:00:00
Proceeding: CVPR 2025 1
Tasks: Decoder

This paper presents MetricGrids, a novel grid-based neural representation that combines elementary metric grids in various metric spaces to approximate complex nonlinear signals. While grid-based representations are widely adopted for their efficiency and scalability, the existing feature grids with linear indexing for continuous-space points can only provide degenerate linear latent space representations, and such representations cannot be adequately compensated to represent complex nonlinear signals by the following compact decoder. To address this problem while keeping the simplicity of a regular grid structure, our approach builds upon the standard grid-based paradigm by constructing multiple elementary metric grids as high-order terms to approximate complex nonlinearities, following the Taylor expansion principle. Furthermore, we enhance model compactness with hash encoding based on different sparsities of the grids to prevent detrimental hash collisions, and a high-order extrapolation decoder to reduce explicit grid storage requirements. experimental results on both 2D and 3D reconstructions demonstrate the superior fitting and rendering accuracy of the proposed method across diverse signal types, validating its robustness and generalizability. Code is available at https://github.com/wangshu31/MetricGrids}{https://github.com/wangshu31/MetricGrids.
Reference-Free 3D Reconstruction of Brain Dissection Photographs with Machine Learning
Authors: Lin Tian, Sean I. Young, Jonathan Williams Ramirez, Dina Zemlyanker, Lucas Jacob Deden Binder, Rogeny Herisse, Theresa R. Connors, Derek H. Oakley, Bradley T. Hyman, Oula Puonti, Matthew S. Rosen, Juan Eugenio Iglesias
Date: 2025-03-13 00:00:00
Tasks: 3D Reconstruction

Correlation of neuropathology with MRI has the potential to transfer microscopic signatures of pathology to invivo scans. Recently, a classical registration method has been proposed, to build these correlations from 3D reconstructed stacks of dissection photographs, which are routinely taken at brain banks. These photographs bypass the need for exvivo MRI, which is not widely accessible. However, this method requires a full stack of brain slabs and a reference mask (e.g., acquired with a surface scanner), which severely limits the applicability of the technique. Here we propose RefFree, a dissection photograph reconstruction method without external reference. RefFree is a learning approach that estimates the 3D coordinates in the atlas space for every pixel in every photograph; simple least-squares fitting can then be used to compute the 3D reconstruction. As a by-product, RefFree also produces an atlas-based segmentation of the reconstructed stack. RefFree is trained on synthetic photographs generated from digitally sliced 3D MRI data, with randomized appearance for enhanced generalization ability. Experiments on simulated and real data show that RefFree achieves performance comparable to the baseline method without an explicit reference while also enabling reconstruction of partial stacks. Our code is available at https://github.com/lintian-a/reffree.
Resolution Invariant Autoencoder
Authors: Ashay Patel, Michela Antonelli, Sebastien Ourselin, M. Jorge Cardoso
Date: 2025-03-12 00:00:00
Tasks: Super-Resolution

Deep learning has significantly advanced medical imaging analysis, yet variations in image resolution remain an overlooked challenge. Most methods address this by resampling images, leading to either information loss or computational inefficiencies. While solutions exist for specific tasks, no unified approach has been proposed. We introduce a resolution-invariant autoencoder that adapts spatial resizing at each layer in the network via a learned variable resizing process, replacing fixed spatial down/upsampling at the traditional factor of 2. This ensures a consistent latent space resolution, regardless of input or output resolution. Our model enables various downstream tasks to be performed on an image latent whilst maintaining performance across different resolutions, overcoming the shortfalls of traditional methods. We demonstrate its effectiveness in uncertainty-aware super-resolution, classification, and generative modelling tasks and show how our method outperforms conventional baselines with minimal performance loss across resolutions.
Bidirectional Learned Facial Animation Codec for Low Bitrate Talking Head Videos
Authors: Riku Takahashi, Ryugo Morita, Fuma Kimishima, Kosuke Iwama, Jinjia Zhou
Date: 2025-03-12 00:00:00
Tasks: Video Reconstruction

Existing deep facial animation coding techniques efficiently compress talking head videos by applying deep generative models. Instead of compressing the entire video sequence, these methods focus on compressing only the keyframe and the keypoints of non-keyframes (target frames). The target frames are then reconstructed by utilizing a single keyframe, and the keypoints of the target frame. Although these unidirectional methods can reduce the bitrate, they rely on a single keyframe and often struggle to capture large head movements accurately, resulting in distortions in the facial region. In this paper, we propose a novel bidirectional learned animation codec that generates natural facial videos using past and future keyframes. First, in the Bidirectional Reference-Guided Auxiliary Stream Enhancement (BRG-ASE) process, we introduce a compact auxiliary stream for non-keyframes, which is enhanced by adaptively selecting one of two keyframes (past and future). This stream improves video quality with a slight increase in bitrate. Then, in the Bidirectional Reference-Guided Video Reconstruction (BRG-VRec) process, we animate the adaptively selected keyframe and reconstruct the target frame using both the animated keyframe and the auxiliary frame. Extensive experiments demonstrate a 55% bitrate reduction compared to the latest animation based video codec, and a 35% bitrate reduction compared to the latest video coding standard, Versatile Video Coding (VVC) on a talking head video dataset. It showcases the efficiency of our approach in improving video quality while simultaneously decreasing bitrate.
4D-ACFNet: A 4D Attention Mechanism-Based Prognostic Framework for Colorectal Cancer Liver Metastasis Integrating Multimodal Spatiotemporal Features
Authors: Zesheng Li, Wei Yang, Yan Su, Yiran Zhu, Yuhan Tang, Haoran Chen, Chengchang Pan, Honggang Qi
Date: 2025-03-12 00:00:00
Tasks: cross-modal alignment, Disentanglement

Postoperative prognostic prediction for colorectal cancer liver metastasis (CRLM) remains challenging due to tumor heterogeneity, dynamic evolution of the hepatic microenvironment, and insufficient multimodal data fusion. To address these issues, we propose 4D-ACFNet, the first framework that synergistically integrates lightweight spatiotemporal modeling, cross-modal dynamic calibration, and personalized temporal prediction within a unified architecture. Specifically, it incorporates a novel 4D spatiotemporal attention mechanism, which employs spatiotemporal separable convolution (reducing parameter count by 41%) and virtual timestamp encoding to model the interannual evolution patterns of postoperative dynamic processes, such as liver regeneration and steatosis. For cross-modal feature alignment, Transformer layers are integrated to jointly optimize modality alignment loss and disentanglement loss, effectively suppressing scale mismatch and redundant interference in clinical-imaging data. Additionally, we design a dynamic prognostic decision module that generates personalized interannual recurrence risk heatmaps through temporal upsampling and a gated classification head, overcoming the limitations of traditional methods in temporal dynamic modeling and cross-modal alignment. Experiments on 197 CRLM patients demonstrate that the model achieves 100% temporal adjacency accuracy (TAA), with performance significantly surpassing existing approaches. This study establishes the first spatiotemporal modeling paradigm for postoperative dynamic monitoring of CRLM. The proposed framework can be extended to prognostic analysis of multi-cancer metastases, advancing precision surgery from "spatial resection" to "spatiotemporal cure."
Rel-UNet: Reliable Tumor Segmentation via Uncertainty Quantification in nnU-Net
Authors: Seyed Sina Ziaee, Farhad Maleki, Katie Ovens
Date: 2025-03-11 00:00:00
Tasks: Computational Efficiency, Decision Making, Segmentation, Tumor Segmentation, Uncertainty Quantification

Accurate and reliable tumor segmentation is essential in medical imaging analysis for improving diagnosis, treatment planning, and monitoring. However, existing segmentation models often lack robust mechanisms for quantifying the uncertainty associated with their predictions, which is essential for informed clinical decision-making. This study presents a novel approach for uncertainty quantification in kidney tumor segmentation using deep learning, specifically leveraging multiple local minima during training. Our method generates uncertainty maps without modifying the original model architecture or requiring extensive computational resources. We evaluated our approach on the KiTS23 dataset, where our approach effectively identified ambiguous regions faster and with lower uncertainty scores in contrast to previous approaches. The generated uncertainty maps provide critical insights into model confidence, ultimately enhancing the reliability of the segmentation with the potential to support more accurate medical diagnoses. The computational efficiency and model-agnostic design of the proposed approach allows adaptation without architectural changes, enabling use across various segmentation models.
Phasor-Pursuit Directional Modulation
Authors: David Couto, Arash Samani, Alec Yonika
Date: 2025-03-13 00:00:00

Emitting phased array RF systems have to contend with an ever-increasing number of eavesdroppers as technological advancements provide lower cost and/or more capable radios. Often, eavesdroppers can accumulate sufficient information transmitted in sidelobes by integrating over long enough periods. Directional modulation (DM) disrupts this capability by inducing a randomized walk through IQ-space to reach a pertinent location which corresponds to a symbol of particular amplitude and phase. This results in higher secrecy capacity. The path taken by the cumulative element contributions are determined by the complex weights of individual transmitters. Because large phased arrays support a large number of available paths, repetitions of applied weights are not concerning. The same cannot be said for arrays that consist of only a few elements, e.g. WiFi routers. We introduce a computationally efficient and flexible framework for DM that utilizes real-valued phase rotations of element weights. It supports a wide family of modulation schemes in phase and/or amplitude. Using state-of-the-practice orthogonal noise injection framework, we demonstrate a richness of unique paths resolving concerns about repeated weights. In our proposed scheme, there is a small reduction in received power compared to traditional beamforming, as little as 1 dB, which is an advantage over conventional directional modulation which typically sacrifices 6 dB of power. Also, there is a significantly larger set of possible element weights than that of the conventional scheme. This feature protects against the possibility of eavesdroppers breaking the distortion-based obfuscation of symbols over repeated observations. Our approach provides more secrecy capacity than conventional directional modulation via increased power delivery and increased receiver SNR and does so with resilience to advanced eavesdropping threats.
BioSerenity-E1: a self-supervised EEG model for medical applications
Authors: Ruggero G. Bettinardi, Mohamed Rahmouni, Ulysse Gimenez
Date: 2025-03-13 00:00:00
Tasks: Diagnostic, EEG, Seizure Detection, Self-Supervised Learning

Electroencephalography (EEG) serves as an essential diagnostic tool in neurology; however, its accurate manual interpretation is a time-intensive process that demands highly specialized expertise, which remains relatively scarce and not consistently accessible. To address these limitations, the implementation of automated pre-screening and analysis systems for EEG data holds considerable promise. Advances in self-supervised learning made it possible to pre-train complex deep learning architectures on large volumes of unlabeled EEG data to learn generalizable representations, that can later be used to enhance performance on multiple tasks while needing less downstream data. In the present paper, we introduce BioSerenity-E1, the first of a family of self-supervised foundation models for clinical EEG applications that combines spectral tokenization with masked prediction to achieve state-of-the-art performance across relevant diagnostic tasks. The two-phase self-supervised pretraining framework initially acquires compressed EEG representations via a transformer-based VQ-VAE architecture designed to reconstruct log-multitaper spectral projections, then implements extensive (70% block) masked token prediction to force the model to learn complex spatiotemporal dependencies in EEG signals. BioSerenity-E1 achieves strong performance across three clinical tasks, either in line or above state-of-the-art methods: seizure detection (AUROC = 0.926, Sensitivity = 0.909), normal/abnormal classification (AUPRC = 0.970 on proprietary data; 0.910 on TUH-Abnormal), and multiclass pathology differentiation on unbalanced data (Weighted F1 = 0.730). The utility of BioSerenity-E1 is further confirmed in low-data regimes scenarios, showing clear improvements in AUPRC (from +2% to 17%) when trained on less than 10% of the available data.
Mirror Online Conformal Prediction with Intermittent Feedback
Authors: Bowen Wang, Matteo Zecchin, Osvaldo Simeone
Date: 2025-03-13 00:00:00
Tasks: Conformal Prediction, Prediction

Online conformal prediction enables the runtime calibration of a pre-trained artificial intelligence model using feedback on its performance. Calibration is achieved through set predictions that are updated via online rules so as to ensure long-term coverage guarantees. While recent research has demonstrated the benefits of incorporating prior knowledge into the calibration process, this has come at the cost of replacing coverage guarantees with less tangible regret guarantees based on the quantile loss. This work introduces intermittent mirror online conformal prediction (IM-OCP), a novel runtime calibration framework that integrates prior knowledge, while maintaining long-term coverage and achieving sub-linear regret. IM-OCP features closed-form updates with minimal memory complexity, and is designed to operate under potentially intermittent feedback.
Liquid Lens-Based Imaging Receiver for MIMO VLC Systems
Authors: Kapila W. S. Palitharathna, Christodoulos Skouroumounis, Ioannis Krikidis
Date: 2025-03-13 00:00:00

In this paper, we consider a tunable liquid convex lens-assisted imaging receiver for indoor multiple-input multiple-output (MIMO) visible light communication (VLC) systems. In contrast to existing MIMO VLC receivers that rely on fixed optical lenses, the proposed receiver leverages the additional degrees of freedom offered by liquid lenses via adjusting both focal length and orientation angles of the lens. This capability facilitates the mitigation of spatial correlation between the channel gains, thereby enhancing the overall signal quality and leading to improved bit-error rate (BER) performance. We present an accurate channel model for the liquid lens-assisted VLC system by using three-dimensional geometry and geometric optics. To achieve optimal performance under practical conditions such as random receiver orientation and user mobility, optimization of both focal length and orientation angles of the lens are required. To this end, driven by the fact that channel models are mathematically complex, we present two optimization schemes including a blockwise machine learning (ML) architecture that includes convolution layers to extract spatial features from the received signal, long-short term memory layers to predict the user position and orientation, and fully connected layers to estimate the optimal lens parameters. Numerical results are presented to compare the performance of each scheme with conventional receivers. Results show that a significant BER improvement is achieved when liquid lenses and presented ML-based optimization approaches are used. Specifically, the BER can be improved from $6\times 10^{-2}$ to $1.4\times 10^{-3}$ at an average signal-to-noise ratio of $30$ dB.
CoDiPhy: A General Framework for Applying Denoising Diffusion Models to the Physical Layer of Wireless Communication Systems
Authors: Peyman Neshaastegaran, Ming Jian
Date: 2025-03-13 00:00:00
Tasks: Denoising

Generative models, including denoising diffusion models (DM), are gaining attention in wireless applications due to their ability to learn complex data distributions. In this paper, we propose CoDiPhy, a novel framework that leverages conditional denoising diffusion models to address a wide range of wireless physical layer problems. A key challenge of using DM is the need to assume or approximate Gaussian signal models. CoDiPhy addresses this by incorporating a conditional encoder as a guidance mechanism, mapping problem observations to a latent space and removing the Gaussian constraint. By combining conditional encoding, time embedding layers, and a U-Net-based main neural network, CoDiPhy introduces a noise prediction neural network, replacing the conventional approach used in DM. This adaptation enables CoDiPhy to serve as an effective solution for a wide range of detection, estimation, and predistortion tasks. We demonstrate CoDiPhy's adaptability through two case studies: an OFDM receiver for detection and phase noise compensation for estimation. In both cases, CoDiPhy outperforms conventional methods by a significant margin.
Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments
Authors: Ali Elkeshawy, Haifa Fares, Amor Nafkha
Date: 2025-03-13 00:00:00
Tasks: Action Detection, Activity Detection, CPU

Massive MIMO is considered a key enabler to support massive machine-type communication (mMTC). While massive access schemes have been extensively analyzed for co-located massive MIMO arrays, this paper explores activity detection in grant-free random access for mMTC within the context of cell-free massive MIMO systems, employing distributed antenna arrays. This sparse support recovery of device activity status is performed by a finite cluster of access points (APs) from a large number of geographically distributed APs collaborating to serve a larger number of devices. Active devices transmit non-orthogonal pilot sequences to APs, which forward the received signals to a central processing unit (CPU) for collaborative activity detection. This paper proposes a simple and efficient data-driven algorithm tailored for device activity detection, implemented centrally at the CPU. Furthermore, the study assesses the algorithm's robustness to input perturbations and examines the effects of adopting fixed-point representation on its performance.
Optimal Privacy-Preserving Distributed Median Consensus
Authors: Wenrui Yu, Qiongxiu Li, Richard Heusdens, Sokol Kosta
Date: 2025-03-13 00:00:00
Tasks: Privacy Preserving

Distributed median consensus has emerged as a critical paradigm in multi-agent systems due to the inherent robustness of the median against outliers and anomalies in measurement. Despite the sensitivity of the data involved, the development of privacy-preserving mechanisms for median consensus remains underexplored. In this work, we present the first rigorous analysis of privacy in distributed median consensus, focusing on an $L_1$-norm minimization framework. We establish necessary and sufficient conditions under which exact consensus and perfect privacy-defined as zero information leakage-can be achieved simultaneously. Our information-theoretic analysis provides provable guarantees against passive and eavesdropping adversaries, ensuring that private data remain concealed. Extensive numerical experiments validate our theoretical results, demonstrating the practical feasibility of achieving both accuracy and privacy in distributed median consensus.
An LiGME Regularizer of Designated Isolated Minimizers -- An Application to Discrete-Valued Signal Estimation
Authors: Satoshi Shoji, Wataru Yata, Keita Kume, Isao Yamada
Date: 2025-03-13 00:00:00

For a regularized least squares estimation of discrete-valued signals, we propose a Linearly involved Generalized Moreau Enhanced (LiGME) regularizer, as a nonconvex regularizer, of designated isolated minimizers. The proposed regularizer is designed as a Generalized Moreau Enhancement (GME) of the so-called sum-of-absolute-values (SOAV) convex regularizer. Every candidate vector in the discrete-valued set is aimed to be assigned to an isolated local minimizer of the proposed regularizer while the overall convexity of the regularized least squares model is maintained. Moreover, a global minimizer of the proposed model can be approximated iteratively by using a variant of the constrained LiGME (cLiGME) algorithm. To enhance the accuracy of the proposed estimation, we also propose a pair of simple modifications, called respectively an iterative reweighting and a generalized superiorization. Numerical experiments demonstrate the effectiveness of the proposed model and algorithms in a scenario of multiple-input multiple-output (MIMO) signal detection.
Sum-Rate Maximization for Pinching Antenna-assisted NOMA Systems with Multiple Dielectric Waveguides
Authors: Shaokang Hu, Ruotong Zhao, Yihuan Liao, Derrick Wing Kwan Ng, Jinhong Yuan
Date: 2025-03-13 00:00:00

This paper investigates the resource allocation design for a pinching antenna (PA)-assisted multiuser multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system featuring multiple dielectric waveguides. To enhance model accuracy, we propose a novel frequency-dependent power attenuation model for the dielectric waveguides in PA-assisted systems. By jointly optimizing the precoder vector and the PA placement, we aim to maximize the system's sum-rate while accounting for the power attenuation across the dielectric waveguides. The design is formulated as a non-convex optimization problem. To effectively address the problem at hand, we introduce an alternating optimization-based algorithm to obtain a suboptimal solution in polynomial time. Our results demonstrate that the proposed PA-assisted system not only significantly outperforms the conventional system but also surpasses a naive PA-assisted system that disregards power attenuation. The performance gain compared to the naive PA-assisted system becomes more pronounced at high carrier frequencies, emphasizing the importance of considering power attenuation in system design.
A New Interpretation of the Time-Interleaved ADC Mismatch Problem: A Tracking-Based Hybrid Calibration Approach
Authors: Jiwon Sung, Jinseok Choi
Date: 2025-03-13 00:00:00

Time-interleaved ADCs (TI-ADCs) achieve high sampling rates by interleaving multiple sub-ADCs in parallel. Mismatch errors between the sub-ADCs, however, can significantly degrade the signal quality, which is a main performance bottleneck. This paper presents a hybrid calibration approach by interpreting the mismatch problem as a tracking problem, and uses the extended Kalman filter for online estimation and compensation of the mismatch errors. After estimation, the desired signal is reconstructed using a truncated fractional delay filter and a high-pass filter. Simulations demonstrate that our algorithm substantially outperforms the existing hybrid calibration method in both mismatch estimation and compensation.