pwc-archive/papers-with-abstracts huggingface dataset:
Advancing Chronic Tuberculosis Diagnostics Using Vision-Language Models: A Multi modal Framework for Precision Analysis
Authors: Praveen Shastry, Sowmya Chowdary Muthulur, Naveen Kumarasami, Anandakumar D, Mounigasri M, Keerthana R, Kishore Prasath Venkatesh, Bargava Subramanian, Kalyan Sivasailam, Revathi Ezhumalai, Abitha Marimuthu
Date: 2025-03-17 00:00:00
Tasks: Decoder, Diagnostic

Background: This study proposes a Vision-Language Model (VLM) leveraging the SIGLIP encoder and Gemma-3b transformer decoder to enhance automated chronic tuberculosis (TB) screening. By integrating chest X-ray images with clinical data, the model addresses the challenges of manual interpretation, improving diagnostic consistency and accessibility, particularly in resource-constrained settings. Methods: The VLM architecture combines a Vision Transformer (ViT) for visual encoding and a transformer-based text encoder to process clinical context, such as patient histories and treatment records. Cross-modal attention mechanisms align radiographic features with textual information, while the Gemma-3b decoder generates comprehensive diagnostic reports. The model was pre-trained on 5 million paired medical images and texts and fine-tuned using 100,000 chronic TB-specific chest X-rays. Results: The model demonstrated high precision (94 percent) and recall (94 percent) for detecting key chronic TB pathologies, including fibrosis, calcified granulomas, and bronchiectasis. Area Under the Curve (AUC) scores exceeded 0.93, and Intersection over Union (IoU) values were above 0.91, validating its effectiveness in detecting and localizing TB-related abnormalities. Conclusion: The VLM offers a robust and scalable solution for automated chronic TB diagnosis, integrating radiographic and clinical data to deliver actionable and context-aware insights. Future work will address subtle pathologies and dataset biases to enhance the model's generalizability, ensuring equitable performance across diverse populations and healthcare settings.
Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light Scenarios
Authors: Huaqiu Li, Xiaowan Hu, Haoqian Wang
Date: 2025-03-17 00:00:00
Tasks: Denoising

Real-world low-light images often suffer from complex degradations such as local overexposure, low brightness, noise, and uneven illumination. Supervised methods tend to overfit to specific scenarios, while unsupervised methods, though better at generalization, struggle to model these degradations due to the lack of reference images. To address this issue, we propose an interpretable, zero-reference joint denoising and low-light enhancement framework tailored for real-world scenarios. Our method derives a training strategy based on paired sub-images with varying illumination and noise levels, grounded in physical imaging principles and retinex theory. Additionally, we leverage the Discrete Cosine Transform (DCT) to perform frequency domain decomposition in the sRGB space, and introduce an implicit-guided hybrid representation strategy that effectively separates intricate compounded degradations. In the backbone network design, we develop retinal decomposition network guided by implicit degradation representation mechanisms. Extensive experiments demonstrate the superiority of our method. Code will be available at https://github.com/huaqlili/unsupervised-light-enhance-ICLR2025.
Ship Detection in Remote Sensing Imagery for Arbitrarily Oriented Object Detection
Authors: Bibi Erum Ayesha, T. Satyanarayana Murthy, Palamakula Ramesh Babu, Ramu Kuchipudi
Date: 2025-03-17 00:00:00
Tasks: Disaster Response, Instance Segmentation, object-detection, Object Detection, Oriented Object Detection, Semantic Segmentation

This research paper presents an innovative ship detection system tailored for applications like maritime surveillance and ecological monitoring. The study employs YOLOv8 and repurposed U-Net, two advanced deep learning models, to significantly enhance ship detection accuracy. Evaluation metrics include Mean Average Precision (mAP), processing speed, and overall accuracy. The research utilizes the "Airbus Ship Detection" dataset, featuring diverse remote sensing images, to assess the models' versatility in detecting ships with varying orientations and environmental contexts. Conventional ship detection faces challenges with arbitrary orientations, complex backgrounds, and obscured perspectives. Our approach incorporates YOLOv8 for real-time processing and U-Net for ship instance segmentation. Evaluation focuses on mAP, processing speed, and overall accuracy. The dataset is chosen for its diverse images, making it an ideal benchmark. Results demonstrate significant progress in ship detection. YOLOv8 achieves an 88% mAP, excelling in accurate and rapid ship detection. U Net, adapted for ship instance segmentation, attains an 89% mAP, improving boundary delineation and handling occlusions. This research enhances maritime surveillance, disaster response, and ecological monitoring, exemplifying the potential of deep learning models in ship detection.
Distributed Algorithm for Cooperative Joint Localization and Tracking Using Multiple-Input Multiple-Output Radars
Authors: Astrid Holm Filtenborg Kitchen, Mikkel Sebastian Lundsgaard Brøndt, Marie Saugstrup Jensen, Troels Pedersen, Anders Malthe Westerkam
Date: 2025-03-20 00:00:00

We propose a distributed joint localization and tracking algorithm using a message passing framework, for multiple-input multiple-output radars. We employ the mean field approach to derive an iterative algorithm. The obtained algorithm features a small communication overhead that scales linearly with the number of radars in the system. The proposed algorithm shows good estimation accuracy in two simulated scenarios even below 0 dB signal to noise ratio. In both cases the ground truth falls within the 95 % confidence interval of the estimated posterior for the majority of the track.
Nonparametric Bellman Mappings for Value Iteration in Distributed Reinforcement Learning
Authors: Yuki Akiyama, Konstantinos Slavakis
Date: 2025-03-20 00:00:00

This paper introduces novel Bellman mappings (B-Maps) for value iteration (VI) in distributed reinforcement learning (DRL), where multiple agents operate over a network without a centralized fusion node. Each agent constructs its own nonparametric B-Map for VI while communicating only with direct neighbors to achieve consensus. These B-Maps operate on Q-functions represented in a reproducing kernel Hilbert space, enabling a nonparametric formulation that allows for flexible, agent-specific basis function design. Unlike existing DRL methods that restrict information exchange to Q-function estimates, the proposed framework also enables agents to share basis information in the form of covariance matrices, capturing additional structural details. A theoretical analysis establishes linear convergence rates for both Q-function and covariance-matrix estimates toward their consensus values. The optimal learning rates for consensus-based updates are dictated by the ratio of the smallest positive eigenvalue to the largest one of the network's Laplacian matrix. Furthermore, each nodal Q-function estimate is shown to lie very close to the fixed point of a centralized nonparametric B-Map, effectively allowing the proposed DRL design to approximate the performance of a centralized fusion center. Numerical experiments on two well-known control problems demonstrate the superior performance of the proposed nonparametric B-Maps compared to prior methods. Notably, the results reveal a counter-intuitive finding: although the proposed approach involves greater information exchange -- specifically through the sharing of covariance matrices -- it achieves the desired performance with lower cumulative communication cost than existing DRL schemes, highlighting the crucial role of basis information in accelerating the learning process.
Learning Linear Block Codes with Gradient Quantization
Authors: Louis-Adrien Dufrène, Quentin Lampin, Guillaume Larue
Date: 2025-03-20 00:00:00
Tasks: Decoder, Quantization

This study investigates the problem of learning linear block codes optimized for Belief-Propagation decoders significantly improving performance compared to the state-of-the-art. Our previous research is extended with an enhanced system design that facilitates a more effective learning process for the parity check matrix. We simplify the input dataset, restrict the number of parameters to learn and improve the gradient back-propagation within the model. We also introduce novel optimizers specifically designed for discrete-valued weights. Based on conventional gradient computation, these optimizers provide discrete weights updates, enabling finer control and improving explainability of the learning process. Through these changes, we consistently achieve improved code performance, provided appropriately chosen hyper-parameters. To rigorously evaluate the performance of learned codes in the context of short to medium block lengths, we propose a comprehensive code performance assessment framework. This framework enables a fair comparison between our learning methodology and random search approaches, ensuring statistical significance in our results. The proposed model pave the way for a new approach to the efficient learning of linear block codes tailored to specific decoder structures.
Age of Information in Multi-Relay Networks with Maximum Age Scheduling
Authors: Gabriel Martins de Jesus, Felippe Moraes Pereira, João Luiz Rebelatto, Richard Demo Souza, Onel Alcaraz López
Date: 2025-03-20 00:00:00
Tasks: Scheduling

We propose and evaluate age of information (AoI)-aware multiple access mechanisms for the Internet of Things (IoT) in multi-relay two-hop networks. The network considered comprises end devices (EDs) communicating with a set of relays in ALOHA fashion, with new information packets to be potentially transmitted every time slot. The relays, in turn, forward the collected packets to an access point (AP), the final destination of the information generated by the EDs. More specifically, in this work we investigate the performance of four age-aware algorithms that prioritize older packets to be transmitted, namely max-age matching (MAM), iterative max-age scheduling (IMAS), age-based delayed request (ABDR), and buffered ABDR (B-ABDR). The former two algorithms are adapted into the multi-relay setup from previous research, and achieve satisfactory average AoI and average peak AoI performance, at the expense of a significant amount of information exchange between the relays and the AP. The latter two algorithms are newly proposed to let relays decide which one(s) will transmit in a given time slot, requiring less signaling than the former algorithms. We provide an analytical formulation for the AoI lower bound performance, compare the performance of all algorithms in this set-up, and show that they approach the lower bound. The latter holds especially true for B-ABDR, which approaches the lower bound the most closely, tilting the scale in its favor, as it also requires far less signaling than MAM and IMAS.
SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay
Authors: Hyungjun Yoon, Jaehyun Kwak, Biniyam Aschalew Tolera, Gaole Dai, Mo Li, Taesik Gong, Kimin Lee, Sung-Ju Lee
Date: 2024-03-29 00:00:00
Tasks: Computational Efficiency, Domain Adaptation, Meta-Learning, Self-Supervised Learning

Self-supervised learning has emerged as a method for utilizing massive unlabeled data for pre-training models, providing an effective feature extractor for various mobile sensing applications. However, when deployed to end-users, these models encounter significant domain shifts attributed to user diversity. We investigate the performance degradation that occurs when self-supervised models are fine-tuned in heterogeneous domains. To address the issue, we propose SelfReplay, a few-shot domain adaptation framework for personalizing self-supervised models. SelfReplay proposes self-supervised meta-learning for initial model pre-training, followed by a user-side model adaptation by replaying the self-supervision with user-specific data. This allows models to adjust their pre-trained representations to the user with only a few samples. Evaluation with four benchmarks demonstrates that SelfReplay outperforms existing baselines by an average F1-score of 8.8%p. Our on-device computational overhead analysis on a commodity off-the-shelf (COTS) smartphone shows that SelfReplay completes adaptation within an unobtrusive latency (in three minutes) with only a 9.54% memory consumption, demonstrating the computational efficiency of the proposed method.
Towards Achieving Perfect Multimodal Alignment
Authors: Abhi Kamboj, Minh N. Do
Date: 2025-03-19 00:00:00
Tasks: Action Recognition, Representation Learning, Temporal Action Localization

Multimodal alignment constructs a joint latent vector space where modalities representing the same concept map to neighboring latent vectors. We formulate this as an inverse problem and show that, under certain conditions, paired data from each modality can map to equivalent latent vectors, which we refer to as perfect alignment. When perfect alignment cannot be achieved, it can be approximated using the Singular Value Decomposition (SVD) of a multimodal data matrix. Experiments on synthetic multimodal Gaussian data verify the effectiveness of our perfect alignment method compared to a learned contrastive alignment method. We further demonstrate the practical application of cross-modal transfer for human action recognition, showing that perfect alignment significantly enhances the model's accuracy. We conclude by discussing how these findings can be applied to various modalities and tasks and the limitations of our method. We hope these findings inspire further exploration of perfect alignment and its applications in representation learning.
A Sigma Point-based Low Complexity Algorithm for Multipath-based SLAM in MIMO Systems
Authors: Anna Masiero, Alexander Venus, Erik Leitinger
Date: 2025-03-19 00:00:00
Tasks: Simultaneous Localization and Mapping

Multipath-based simultaneous localization and mapping (MP-SLAM) is a promising approach in wireless networks to jointly obtain position information of transmitters/receivers and information of the propagation environment. MP-SLAM models specular reflections at flat surfaces as virtual anchors (VAs), which are mirror images of base stations. Particle-based methods offer high flexibility and can approximate posterior probability density functions of the mobile agent state and the map feature states, (i.e., VA states) with complex shapes. However, they often require a large number of particles to counteract degeneracy in high-dimensional parameter spaces, leading to high computational complexity. Conversely using an insufficient number of particles leads to reduced estimation accuracy. In this paper, we introduce a low-complexity MP-SLAM algorithm using a sigma point (SP)-based implementation of the sum-product algorithm (SPA). We model the messages of continuous states of the agent and the VAs as Gaussian distributions and approximate nonlinearities via SP-transformations. This approach substantially reduces the computational complexity without decreasing accuracy. Since probabilistic data association yields Gaussian mixtures for the agent and VA states, we use moment matching to combine each mixture into a single Gaussian. Numerical results using synthetic and real data demonstrate that our method achieves significantly reduced computational runtimes compared to particle-based schemes, while exhibiting comparable (or even superior) localization and mapping performance.
Satellite Selection for In-Band Coexistence of Dense LEO Networks
Authors: Eunsun Kim, Ian P. Roberts, Taekyun Lee, Jeffrey G. Andrews
Date: 2025-03-19 00:00:00

We study spectrum sharing between two dense low-earth orbit (LEO) satellite constellations, an incumbent primary system and a secondary system that must respect interference protection constraints on the primary system. In particular, we propose a secondary satellite selection framework and algorithm that maximizes capacity while guaranteeing that the time-average interference and absolute interference inflicted upon each primary ground user never exceeds specified thresholds. We solve this NP-hard constrained, combinatorial satellite selection problem through Lagrangian relaxation to decompose it into simpler problems which can then be solved through subgradient methods. A high-fidelity simulation is developed based on public FCC filings and technical specifications of the Starlink and Kuiper systems. We use this case study to illustrate the effectiveness of our approach and that explicit protection is indeed necessary for healthy coexistence. We further demonstrate that deep learning models can be used to predict the primary satellite system associations, which helps the secondary system avoid inflicting excessive interference and maximize its own capacity.
Joint Hybrid Precoding and Multi-IRS Optimization for mmWave MU-MISO Communication Network
Authors: Fardad Rahkheir, Soroush Akhlaghi
Date: 2025-03-19 00:00:00

This paper attempts to jointly optimize the hybrid precoding (HP) and intelligent reflecting surfaces (IRS) beamforming matrices in a multi-IRS-aided mmWave communication network, utilizing the Alamouti scheme at the base station (BS). Considering the overall signal-to-noise ratio (SNR) as the objective function, the underlying problem is cast as an optimization problem, which is shown to be non-convex in general. To tackle the problem, noting that the unknown matrices contribute multiplicatively to the objective function, they are reformulated into two new matrices with rank constraints. Then, using the so-called inner approximation (IA) technique in conjunction with majorization-minimization (MM) approaches, these new matrices are solved iteratively. From one of these matrices, the IRS beamforming matrices can be effectively extracted. Meanwhile, HP precoding matrices can be solved separately through a new optimization problem aimed at minimizing the Euclidean distance between the fully digital (FD) precoder and HP analog/digital precoders. This is achieved through the use of a modified block coordinate descent (MBCD) algorithm. Simulation results demonstrate that the proposed algorithm outperforms various benchmark schemes in terms of achieving a higher achievable rate.
Variational Message Passing-based Multiobject Tracking for MIMO-Radars using Raw Sensor Signals
Authors: Anders Malthe Westerkam, Jakob Möderl, Erik Leitinger, Troels Pedersen
Date: 2025-03-19 00:00:00
Tasks: Object, Super-Resolution

In this paper, we propose a direct multiobject tracking (MOT) approach for MIMO-radar signals that operates on raw sensor data via variational message passing (VMP). Unlike classical track-before-detect (TBD) methods, which often rely on simplified likelihood models and exclude nuisance parameters (e.g., object amplitudes, noise variance), our method adopts a superimposed signal model and employs a mean-field approximation to jointly estimate both object existence and object states. By considering correlations within in the radar signal due to closely spaced objects and jointly estimating nuisance parameters, the proposed method achieves robust performance for close-by objects and in low-signal-to-noise ratio (SNR) regimes. Our numerical evaluation based on MIMO-radar signals demonstrate that our VMP-based direct-MOT method outperforms a detect-then-track (DTT) pipeline comprising a super-resolution sparse Bayesian learning (SBL)-based estimation stage followed by classical MOT using global nearest neighbour data association and a Kalman filter.
Sig2text, a Vision-language model for Non-cooperative Radar Signal Parsing
Authors: Hancong Feng KaiLI Jiang Bin tang
Date: 2025-03-19 00:00:00
Tasks: Language Modeling, Language Modelling, parameter estimation

Automatic non-cooperative analysis of intercepted radar signals is essential for intelligent equipment in both military and civilian domains. Accurate modulation identification and parameter estimation enable effective signal classification, threat assessment, and the development of countermeasures. In this paper, we propose a symbolic approach for radar signal recognition and parameter estimation based on a vision-language model that combines context-free grammar with time-frequency representation of radar waveforms. The proposed model, called Sig2text, leverages the power of vision transformers for time-frequency feature extraction and transformer-based decoders for symbolic parsing of radar waveforms. By treating radar signal recognition as a parsing problem, Sig2text can effectively recognize and parse radar waveforms with different modulation types and parameters. We evaluate the performance of Sig2text on a synthetic radar signal dataset and demonstrate its effectiveness in recognizing and parsing radar waveforms with varying modulation types and parameters. The training code of the model is available at https://github.com/Na-choneko/sig2text.
Waveform and Filter Design for Integrated Sensing and Communication Against Signal-dependent Modulated Jamming
Authors: Yu Zhou, Qiao Shi, Zhengchun Zhou, Zilong Liu, Pingzhi Fan
Date: 2025-03-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper focuses on an integrated sensing and communication (ISAC) system in the presence of signal-dependent modulated jamming (SDMJ). Our goal is to suppress jamming while carrying out simultaneous communications and sensing. We minimize the integrated sidelobe level (ISL) of the mismatch filter output for the transmitted waveform and the integrated level (IL) of the mismatch filter output for the jamming, under the constraints of the loss in-processing gain (LPG) and the peak-to-average power ratio (PAPR) of the transmitted waveform. Meanwhile, the similarity constraint is introduced for information-bearing transmit waveform. We develop a decoupled majorization minimization (DMM) algorithm to solve the proposed multi-constrained optimization problem. In contrast to the existing approaches, the proposed algorithm transforms the difficult optimization problem involving two variables into two parallel sub-problems with one variable, thus significantly speeding up the convergence rate. Furthermore, fast Fourier transform (FFT) is introduced to compute the closed-form solution of each sub-problem, giving rise to a greatly reduced computation complexity. Simulation results demonstrate the capabilities of the proposed ISAC system which strikes a proper trade-off among sensing and jamming suppression.
Movable-Element RIS-Aided Wireless Communications: An Element-Wise Position Optimization Approach
Authors: Jingjing Zhao, Qingyi Huang, Kaiquan Cai, Quan Zhou, Xidong Mu, Yuanwei Liu
Date: 2025-03-19 00:00:00
Tasks: Position

A point-to-point movable element (ME) enabled reconfigurable intelligent surface (ME-RIS) communication system is investigated, where each element position can be flexibly adjusted to create favorable channel conditions. For maximizing the communication rate, an efficient ME position optimization approach is proposed. Specifically, by characterizing the cascaded channel power gain in an element-wise manner, the position of each ME is iteratively updated by invoking the successive convex approximation method. Numerical results unveil that 1) the proposed element-wise ME position optimization algorithm outperforms the gradient descent algorithm; and 2) the ME-RIS significantly improves the communication rate compared to the conventional RIS with fixed-position elements.