Tootfinder

Plan C turned out to be a nice day in the mountains. But Woah was there a chill wind. Good that I was prepared.
#hiking #wandern #mountains

Investigating Multi-layer Representations for Dense Passage Retrieval
Zhongbin Xie, Thomas Lukasiewicz
https://arxiv.org/abs/2509.23861 https://arxiv.org/p…

Investigating Multi-layer Representations for Dense Passage Retrieval
Dense retrieval models usually adopt vectors from the last hidden layer of the document encoder to represent a document, which is in contrast to the fact that representations in different layers of a pre-trained language model usually contain different kinds of linguistic knowledge, and behave differently during fine-tuning. Therefore, we propose to investigate utilizing representations from multiple encoder layers to make up the representation of a document, which we denote Multi-layer Represe…

The warm outer layer of a Little Red Dot as the source of [Fe II] and collisional Balmer lines with scattering wings
Alberto Torralba, Jorryt Matthee, Gabriele Pezzulli, Rohan P. Naidu, Yuzo Ishikawa, Gabriel B. Brammer, Seok-Jun Chang, John Chisholm, Anna de Graaff, Francesco D'Eugenio, Claudia Di Cesare, Anna-Christina Eilers, Jenny E. Greene, Max Gronke, Edoardo Iani, Vasily Kokorev, Gauri Kotiwale, Ivan Kramarenko, Yilun Ma, Sara Mascia, Benjam\'in Navarrete, Erica Nelson, …

The warm outer layer of a Little Red Dot as the source of [Fe II] and collisional Balmer lines with scattering wings
The population of the Little Red Dots (LRDs) may represent a key phase of supermassive black hole (SMBH) growth. A cocoon of dense excited gas is emerging as key component to explain the most striking properties of LRDs, such as strong Balmer breaks and Balmer absorption, as well as the weak IR emission. To dissect the structure of LRDs, we analyze new deep JWST/NIRSpec PRISM and G395H spectra of FRESCO-GN-9771, one of the most luminous known LRDs at $z=5.5$. These reveal a strong Balmer break,…

Puget Sound marine layer with an extremely well defined edge. Maybe a hundred feet high, and in about the same horizontal distance you go from pea soup to a bright sunny day with clear blue skies

Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
Francisco Javier Esono Nkulu Andong, Qi Min
https://arxiv.org/abs/2509.10163

Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
As sixth-generation (6G) networks move toward ultra-dense, intelligent edge environments, efficient resource management under stringent privacy, mobility, and energy constraints becomes critical. This paper introduces a novel Federated Multi-Agent Reinforcement Learning (Fed-MARL) framework that incorporates cross-layer orchestration of both the MAC layer and application layer for energy-efficient, privacy-preserving, and real-time resource management across heterogeneous edge devices. Each age…

Hey all. Just wanted to post the other two #photos from my recent blog post https://www.franzgraf.de/blog/2025/a-familiar-mountain-never-gets-old/
It's one o…

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
Robiul Islam, Dmitry I. Ignatov, Karl Kaberg, Roman Nabatchikov
https://arxiv.org/abs/2509.14078

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
This study investigates classifier performance across EEG frequency bands using various optimizers and evaluates efficient class prediction for the left and right hemispheres. Three neural network architectures - a deep dense network, a shallow three-layer network, and a convolutional neural network (CNN) - are implemented and compared using the TensorFlow and PyTorch frameworks. Results indicate that the Adagrad and RMSprop optimizers consistently perform well across different frequency bands,…

Simple Projection Variants Improve ColBERT Performance
Benjamin Clavi\'e, Sean Lee, Rikiya Takehi, Aamir Shakir, Makoto P. Kato
https://arxiv.org/abs/2510.12327 https://

Simple Projection Variants Improve ColBERT Performance
Multi-vector dense retrieval methods like ColBERT systematically use a single-layer linear projection to reduce the dimensionality of individual vectors. In this study, we explore the implications of the MaxSim operator on the gradient flows of the training of multi-vector models and show that such a simple linear projection has inherent, if non-critical, limitations in this setting. We then discuss the theoretical improvements that could result from replacing this single-layer projection with …

My take for #FootpathFriday from our vacation in the #schwarzwald / #blackforest Iast year.
#hiking