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Dank Weltraumteleskop Gaia: 3D-Karte der Sternentstehungsgebiete um die Sonne
Weil sie dicht in Staub eingehüllt sind, sind die Regionen, in denen Sterne entstehen, besonders schwer zu erforschen. Nun gibt es eine beeindruckende Karte.

Anomaly Detection in Industrial Control Systems Based on Cross-Domain Representation Learning
Dongyang Zhan, Wenqi Zhang, Lin Ye, Xiangzhan Yu, Hongli Zhang, Zheng He
https://arxiv.org/abs/2509.11786

Anomaly Detection in Industrial Control Systems Based on Cross-Domain Representation Learning
Industrial control systems (ICSs) are widely used in industry, and their security and stability are very important. Once the ICS is attacked, it may cause serious damage. Therefore, it is very important to detect anomalies in ICSs. ICS can monitor and manage physical devices remotely using communication networks. The existing anomaly detection approaches mainly focus on analyzing the security of network traffic or sensor data. However, the behaviors of different domains (e.g., network traffic a…

Query-Focused Extractive Summarization for Sentiment Explanation
Ahmed Moubtahij, Sylvie Ratt\'e, Yazid Attabi, Maxime Dumas
https://arxiv.org/abs/2509.11989 https://…

Query-Focused Extractive Summarization for Sentiment Explanation
Constructive analysis of feedback from clients often requires determining the cause of their sentiment from a substantial amount of text documents. To assist and improve the productivity of such endeavors, we leverage the task of Query-Focused Summarization (QFS). Models of this task are often impeded by the linguistic dissonance between the query and the source documents. We propose and substantiate a multi-bias framework to help bridge this gap at a domain-agnostic, generic level; we then for…

Knowledge-Guided Adaptive Mixture of Experts for Precipitation Prediction
Chen Jiang, Kofi Osei, Sai Deepthi Yeddula, Dongji Feng, Wei-Shinn Ku
https://arxiv.org/abs/2509.11459 …

Knowledge-Guided Adaptive Mixture of Experts for Precipitation Prediction
Accurate precipitation forecasting is indispensable in agriculture, disaster management, and sustainable strategies. However, predicting rainfall has been challenging due to the complexity of climate systems and the heterogeneous nature of multi-source observational data, including radar, satellite imagery, and surface-level measurements. The multi-source data vary in spatial and temporal resolution, and they carry domain-specific features, making it challenging for effective integration in con…

LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
Xuqin Wang, Tao Wu, Yanfeng Zhang, Lu Liu, Dong Wang, Mingwei Sun, Yongliang Wang, Niclas Zeller, Daniel Cremers
https://arxiv.org/abs/2509.08628

LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
Diffusion models excel at generating high-quality outputs but face challenges in data-scarce domains, where exhaustive retraining or costly paired data are often required. To address these limitations, we propose Latent Aligned Diffusion Bridges (LADB), a semi-supervised framework for sample-to-sample translation that effectively bridges domain gaps using partially paired data. By aligning source and target distributions within a shared latent space, LADB seamlessly integrates pretrained source…

Event Horizon Telescope Pattern Speeds in the Visibility Domain
Nicholas S. Conroy, Michi Baub\"ock, Vedant Dhruv, Daeyoung Lee, Chi-kwan Chan, Abhishek V. Joshi, Ben Prather, Charles F. Gammie
https://arxiv.org/abs/2510.08848

Event Horizon Telescope Pattern Speeds in the Visibility Domain
The Event Horizon Telescope is preparing to produce time sequences of black hole images, or movies. In anticipation, we developed an autocorrelation technique to measure apparent rotational motion using the image-domain pattern speed $Ω_p$. Here, we extend this technique to the visibility domain and introduce the visibility amplitude pattern speed $Ω_{\mathrm{VA}}$. We show that in the Illinois v3 library of EHT source models, $Ω_{\mathrm{VA}}$ depends on the source inclination, black hole m…

JND-Guided Light-Weight Neural Pre-Filter for Perceptual Image Coding
Chenlong He, Zijing Dong, Min Li, Zhijian Hao, Leilei Huang, Xiaoyang Zeng, Yibo Fan
https://arxiv.org/abs/2510.10648

JND-Guided Light-Weight Neural Pre-Filter for Perceptual Image Coding
Just Noticeable Distortion (JND)-guided pre-filter is a promising technique for improving the perceptual compression efficiency of image coding. However, existing methods are often computationally expensive, and the field lacks standardized benchmarks for fair comparison. To address these challenges, this paper introduces a twofold contribution. First, we develop and open-source FJNDF-Pytorch, a unified benchmark for frequency-domain JND-Guided pre-filters. Second, leveraging this platform, we …

A Physics-Informed Multi-Source Domain Adaptation Framework for Label-Free Post-Earthquake Damage Assessment
Yifeng Zhang, Xiao Liang
https://arxiv.org/abs/2509.26356 https://…

A Physics-Informed Multi-Source Domain Adaptation Framework for Label-Free Post-Earthquake Damage Assessment
Efficient and intelligent assessment of post-earthquake structural damage is critical for rapid disaster response. While data-driven approaches have shown promise, traditional supervised learning methods rely on extensive labeled datasets, which are often impractical to obtain for damaged structures. To address this limitation, we propose a physics-informed multi-source domain adaptation framework to predict post-earthquake structural damage for a target building without requiring damage labels…

Which Programming Language and Model Work Best With LLM-as-a-Judge For Code Retrieval?
Lucas Roberts, Denisa Roberts
https://arxiv.org/abs/2510.00324 https://

Which Programming Language and Model Work Best With LLM-as-a-Judge For Code Retrieval?
Code search is an important information retrieval application. Benefits of better code search include faster new developer on-boarding, reduced software maintenance, and ease of understanding for large repositories. Despite improvements in search algorithms and search benchmarks, the domain of code search has lagged behind. One reason is the high cost of human annotation for code queries and answers. While humans may annotate search results in general text QA systems, code annotations require s…

PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection
Sijun Dong, Yuxuan Hu, LiBo Wang, Geng Chen, Xiaoliang Meng
https://arxiv.org/abs/2509.09572

PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection
To tackle the prevalence of pseudo changes, the scarcity of labeled samples, and the difficulty of cross-domain generalization in multi-temporal and multi-source remote sensing imagery, we propose PeftCD, a change detection framework built upon Vision Foundation Models (VFMs) with Parameter-Efficient Fine-Tuning (PEFT). At its core, PeftCD employs a weight-sharing Siamese encoder derived from a VFM, into which LoRA and Adapter modules are seamlessly integrated. This design enables highly effici…