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TDoA-Based Self-Supervised Channel Charting with NLoS Mitigation
Mohsen Ahadi, Omid Esrafilian, Florian Kaltenberger, Adeel Malik
https://arxiv.org/abs/2510.08001 https://

TDoA-Based Self-Supervised Channel Charting with NLoS Mitigation
Channel Charting (CC) has emerged as a promising framework for data-driven radio localization, yet existing approaches often struggle to scale globally and to handle the distortions introduced by non-line-of-sight (NLoS) conditions. In this work, we propose a novel CC method that leverages Channel Impulse Response (CIR) data enriched with practical features such as Time Difference of Arrival (TDoA) and Transmission Reception Point (TRP) locations, enabling a self-supervised localization functio…

Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data
Stephen Meisenbacher, Svetlozar Nestorov, Peter Norlander
https://arxiv.org/abs/2510.01470

Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data
Data from online job postings are difficult to access and are not built in a standard or transparent manner. Data included in the standard taxonomy and occupational information database (O*NET) are updated infrequently and based on small survey samples. We adopt O*NET as a framework for building natural language processing tools that extract structured information from job postings. We publish the Job Ad Analysis Toolkit (JAAT), a collection of open-source tools built for this purpose, and demo…

Ergoregion instability in bosonic stars: scalar mode structure, universality, and weakly nonlinear effects
Nils Siemonsen
https://arxiv.org/abs/2510.07468 https://

Ergoregion instability in bosonic stars: scalar mode structure, universality, and weakly nonlinear effects
Ultracompact spinning horizonless spacetimes with ergoregions are subject to the ergoregion instability. We systematically investigate the instability of a massless scalar field in a variety of rapidly spinning Proca stars and boson stars using WKB-, frequency-, and time-domain methods. We find universal features in the mode structure: the onset of the instability is signaled by a zero-mode, the mode frequencies and growth rates are related by a simple scaling relation in the small-frequency li…

Interpreting Language Models Through Concept Descriptions: A Survey
Nils Feldhus, Laura Kopf
https://arxiv.org/abs/2510.01048 https://arxiv.org/pdf/2510.01…

Interpreting Language Models Through Concept Descriptions: A Survey
Understanding the decision-making processes of neural networks is a central goal of mechanistic interpretability. In the context of Large Language Models (LLMs), this involves uncovering the underlying mechanisms and identifying the roles of individual model components such as neurons and attention heads, as well as model abstractions such as the learned sparse features extracted by Sparse Autoencoders (SAEs). A rapidly growing line of work tackles this challenge by using powerful generator mod…

Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning
Julia Matejas, Olaf \.Zurawski, Nils Strodthoff, Juan Miguel Lopez Alcaraz
https://arxiv.org/abs/2509.17674

Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning
Purpose: Chest X-rays are essential for diagnosing pulmonary conditions, but limited access in resource-constrained settings can delay timely diagnosis. Electrocardiograms (ECGs), in contrast, are widely available, non-invasive, and often acquired earlier in clinical workflows. This study aims to assess whether ECG features and patient demographics can predict chest radiograph findings using an interpretable machine learning approach. Methods: Using the MIMIC-IV database, Extreme Gradient Boo…