Tootfinder

Faxgeräte in Rente: Die Geräte haben in vielen Unternehmen ausgedient! 📠 Laut einer aktuellen Bitkom-Umfrage nutzt nicht mal mehr jedes fünfte Unternehmen in Deutschland regelmäßig ein Fax.
Zum Artikel: https://heise.de/-10425507?wt_mc=sm.re…

Decided to blog what I learned about Ubuntu multipass MicroVMs. Enjoy.
#Ubuntu

Ubuntu Multipass MicroVMs
 Ubuntu Multipass MicroVMs Why? Well, I had read about Firecracker MicroVMs, their low overhead, and their fast start times. When I finally...

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025
Shuting Xu, Runtong Liu, Zhixuan Chen, Junlin Hou, Hao Chen
https://arxiv.org/abs/2509.02597 htt…

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025
Deep learning has driven significant advances in mitotic figure analysis within computational pathology. In this paper, we present our approach to the Mitosis Domain Generalization (MIDOG) 2025 Challenge, which consists of two distinct tasks, i.e., mitotic figure detection and atypical mitosis classification. For the mitotic figure detection task, we propose a two-stage detection-classification framework that first localizes candidate mitotic figures and subsequently refines the predictions usi…

Sabes que estšs mesmo a ficar velho quando as notícias sobre a morte de gente mais nova do que tu deixam de te incomodar por serem tão frequentes.
Ainda não estou mesmo a ficar velho.

Scalable Entanglement Detection in Quantum Systems via Fisher Linear Discriminant Analysis
Mahmoud Mahdian, Zahra Mousavi
https://arxiv.org/abs/2509.03233 https://

Scalable Entanglement Detection in Quantum Systems via Fisher Linear Discriminant Analysis
Quantum entanglement is the cornerstone of quantum technology and enables quantum devices to outperform classical systems in terms of performance. However, detecting entanglement in high-dimensional systems remains a significant challenge due to the exponential growth of the Hilbert space with the number of particles. In this work, we use machine learning to classify entangled states and separable states, focusing on the application of classical Fisher Linear Discriminant Analysis (FLDA). By ad…

Interesting early version release of iconclass-vlm, a specialised small vision language model that automatically generates ICONCLASS metadata for cultural heritage collections by Daniel van Strien
iconclass-vlm: https://huggingface.co/davanstrien/iconclass-vlm
model evaluatio…

Vorbild Wasserläufer: Selbstentfaltende Fächer machen den "Rhagobot" wendig
Wasserläufer-Fächer inspirieren Miniroboter. Die Technik soll blitzschnelle Manöver auf dem Wasser ermöglichen.

RF-DETR for Robust Mitotic Figure Detection: A MIDOG 2025 Track 1 Approach
Piotr Giedziun, Jan So{\l}tysik, Mateusz G\'orczany, Norbert Ropiak, Marcin Przymus, Piotr Krajewski, Jaros{\l}aw Kwiecie\'n, Artur Bartczak, Izabela Wasiak, Mateusz Maniewski
https://arxiv.org/abs/2509.02599

RF-DETR for Robust Mitotic Figure Detection: A MIDOG 2025 Track 1 Approach
Mitotic figure detection in histopathology images remains challenging due to significant domain shifts across different scanners, staining protocols, and tissue types. This paper presents our approach for the MIDOG 2025 challenge Track 1, focusing on robust mitotic figure detection across diverse histological contexts. While we initially planned a two-stage approach combining high-recall detection with subsequent classification refinement, time constraints led us to focus on optimizing a single…

Challenges and Lessons from MIDOG 2025: A Two-Stage Approach to Domain-Robust Mitotic Figure Detection
Euiseop Song, Jaeyoung Park, Jaewoo Park
https://arxiv.org/abs/2509.02630 …

Challenges and Lessons from MIDOG 2025: A Two-Stage Approach to Domain-Robust Mitotic Figure Detection
Mitotic figure detection remains a challenging task in computational pathology due to domain variability and morphological complexity. This paper describes our participation in the MIDOG 2025 challenge, focusing on robust mitotic figure detection across diverse tissue domains. We developed a two-stage pipeline combining Faster R-CNN for candidate detection with an ensemble of three classifiers (DenseNet-121, EfficientNet-v2, InceptionResNet-v2) for false positive reduction. Our best submission …