Tootfinder

Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
Mohsen Jenadeleh, Jon Sneyers, Davi Lazzarotto, Shima Mohammadi, Dominik Keller, Atanas Boev, Rakesh Rao Ramachandra Rao, Ant\'onio Pinheiro, Thomas Richter, Alexander Raake, Touradj Ebrahimi, Jo\~ao Ascenso, Dietmar Saupe
https://arxiv…

Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
High dynamic range (HDR) and wide color gamut (WCG) technologies significantly improve color reproduction compared to standard dynamic range (SDR) and standard color gamuts, resulting in more accurate, richer, and more immersive images. However, HDR increases data demands, posing challenges for bandwidth efficiency and compression techniques. Advances in compression and display technologies require more precise image quality assessment, particularly in the high-fidelity range where perceptual…

Pretreatment Liquid Biopsy and Clinicopathologic Features in HPV−Associated Oropharyngeal Squamous Cell Carcinoma (JAMA Otolaryngology)
More advanced clinical T & N stages associated w higher TTMV-HPV DNA in pretreatment blood samples (and further interesting findings)

marvel_universe: Marvel Universe social network
The Marvel Universe collaboration network, where two Marvel characters are considered linked if they jointly appear in the same Marvel comic book.
This network has 19428 nodes and 95497 edges.
Tags: Social, Fictional, Unweighted
https://networks.skewed.de/ne…

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Chuanchao Gao, Niraj Kumar, Arvind Easwaran
https://arxiv.org/abs/2506.12686

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Mobile-edge computing (MEC) has emerged as a promising paradigm for enabling Internet of Things (IoT) devices to handle computation-intensive jobs. Due to the imperfect parallelization of algorithms for job processing on servers and the impact of IoT device mobility on data communication quality in wireless networks, it is crucial to jointly consider server resource allocation and IoT device mobility during job scheduling to fully benefit from MEC, which is often overlooked in existing studies.…

A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems
Kang Zhou, Weixi Zhou, Donghong Cai, Xianfu Lei, Yanqing Xu, Zhiguo Ding, Pingzhi Fan
https://arxiv.org/abs/2506.12583

A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems
In this paper, we consider a novel optimization design for multi-waveguide pinching-antenna systems, aiming to maximize the weighted sum rate (WSR) by jointly optimizing beamforming coefficients and antenna position. To handle the formulated non-convex problem, a gradient-based meta-learning joint optimization (GML-JO) algorithm is proposed. Specifically, the original problem is initially decomposed into two sub-problems of beamforming optimization and antenna position optimization through equi…

Performance Analysis of Communication Signals for Localization in Underwater Sensor Networks
Ashwani Koul, Gustaf Hendeby, Isaac Skog
https://arxiv.org/abs/2506.13330

Performance Analysis of Communication Signals for Localization in Underwater Sensor Networks
Fusion of passive and active measurements from sensor nodes becomes critical in localizing underwater objects and is traditionally achieved by communicating information to a central node. This causes significant inefficiencies in bandwidth, energy, and processing time, which are critical in marine applications. With integrated sensing and communication (ISAC) systems, the process of sensing, localization, and communication can be achieved jointly, and the inefficiencies can be minimized. Thus, …

Bayesian inference for the learning rate in Generalised Bayesian inference
Jeong Eun Lee, Sitong Liu, Geoff K. Nicholls
https://arxiv.org/abs/2506.12532 ht…

Bayesian inference for the learning rate in Generalised Bayesian inference
In Generalised Bayesian Inference (GBI), the learning rate and hyperparameters of the loss must be estimated. However, these inference-hyperparameters can't be estimated jointly with the other parameters by giving them a prior, as we discuss. Several methods for estimating the learning rate have been given which elicit and minimise a loss based on the goals of the overall inference (in our case, prediction of new data). However, in some settings there exists an unknown ``true'' learning rate ab…

TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
Fuaing Niu, Zini Chen, Zhiyu Xie, Genan Dai, Bowen Zhang
https://arxiv.org/abs/2506.13343

TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
User-level stance detection (UserSD) remains challenging due to the lack of high-quality benchmarks that jointly capture linguistic and social structure. In this paper, we introduce TwiUSD, the first large-scale, manually annotated UserSD benchmark with explicit followee relationships, containing 16,211 users and 47,757 tweets. TwiUSD enables rigorous evaluation of stance models by integrating tweet content and social links, with superior scale and annotation quality. Building on this resource,…

Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery
Eugene Kofi Okrah Denteh, Andrews Danyo, Joshua Kofi Asamoah, Blessing Agyei Kyem, Armstrong Aboah
https://arxiv.org/abs/2506.12456

Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery
This study presents a novel demographics informed deep learning framework designed to forecast urban spatial transformations by jointly modeling geographic satellite imagery, socio-demographics, and travel behavior dynamics. The proposed model employs an encoder-decoder architecture with temporal gated residual connections, integrating satellite imagery and demographic data to accurately forecast future spatial transformations. The study also introduces a demographics prediction component which…

Joint Quantile Shrinkage: A State-Space Approach toward Non-Crossing Bayesian Quantile Models
David Kohns, Tibor Szendrei
https://arxiv.org/abs/2506.13257 …

Joint Quantile Shrinkage: A State-Space Approach toward Non-Crossing Bayesian Quantile Models
Crossing of fitted conditional quantiles is a prevalent problem for quantile regression models. We propose a new Bayesian modelling framework that penalises multiple quantile regression functions toward the desired non-crossing space. We achieve this by estimating multiple quantiles jointly with a prior on variation across quantiles, a fused shrinkage prior with quantile adaptivity. The posterior is derived from a decision-theoretic general Bayes perspective, whose form yields a natural state-s…