Tootfinder

No se si visteis esto. Lo que estš haciendo gente para poder programar y que en la empresa piensen que estš usando un agente de loroestocšstico https://danq.me/2026/03/03/ai-agent-logging/

Es ist eher suboptimal, wenn der auf einem Produkt aufgedruckte QR Code beim Webshop zu einem 404 Statuscode führt.
Aus der Reihe "Was man nicht alles bei der Restrukturierung seines (zukünftigen) Webshops bedenken muss".
Liebe Grüsse an den @…

Deutschland versucht, eine souveräne Cloud ohne Google aufzubauen. Google findet das: suboptimal. Und zieht vor die Vergabekammer. Manchmal ist die beste Werbung für digitale Souveränität das Verhalten derer, von denen man unabhängig werden will. FAZ (€). #DigitaleSouveränität #Cloud

Ich finde ja, Online-Petitionen sind ein bislang nur suboptimal genutztes Mittel der #Demokratie. Da geht deutlich mehr!
Das Fediverse (mit seinen überproportional politisch interessierten User:innen) könnte da helfen. Leider ist die Fediverse-Unterstüzung gängiger Petitionsplattformen aber nicht zufriedenstellend (siehe Bilder).
Das ist aber ein lösbares Problem. Z.B. könnte man…

RE: https://flipboard.com/@cbcnews/ottawa-dpigmqdrz/-/a-ApOA11EGSkSHy-B2OkyHQQ:a:107108217-/0
The direct non-translation is suboptimal for communicating their meaning…

T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
Dongik Park, Hyunwoo Ryu, Suahn Bae, Keondo Park, Hyung-Sin Kim
https://arxiv.org/abs/2602.21043 https://arxiv.org/pdf/2602.21043 https://arxiv.org/html/2602.21043
arXiv:2602.21043v1 Announce Type: new
Abstract: Imputing missing values in multivariate time series remains challenging, especially under diverse missing patterns and heavy missingness. Existing methods suffer from suboptimal performance as corrupted temporal features hinder effective cross-variable information transfer, amplifying reconstruction errors. Robust imputation requires both extracting temporal patterns from sparse observations within each variable and selectively transferring information across variables--yet current approaches excel at one while compromising the other. We introduce T1 (Time series imputation with 1-to-1 channel-head binding), a CNN-Transformer hybrid architecture that achieves robust imputation through Channel-Head Binding--a mechanism creating one-to-one correspondence between CNN channels and attention heads. This design enables selective information transfer: when missingness corrupts certain temporal patterns, their corresponding attention pathways adaptively down-weight based on remaining observable patterns while preserving reliable cross-variable connections through unaffected channels. Experiments on 11 benchmark datasets demonstrate that T1 achieves state-of-the-art performance, reducing MSE by 46% on average compared to the second-best baseline, with particularly strong gains under extreme sparsity (70% missing ratio). The model generalizes to unseen missing patterns without retraining and uses a consistent hyperparameter configuration across all datasets. The code is available at https://github.com/Oppenheimerdinger/T1.
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Evaluating Phylogenetic Comparative Methods under Reticulate Evolutionary Scenarios
Lydia Morley, Emma Lehmberg, Sungsik Kong
https://arxiv.org/abs/2603.25986 https://arxiv.org/pdf/2603.25986 https://arxiv.org/html/2603.25986
arXiv:2603.25986v1 Announce Type: new
Abstract: Phylogenetic comparative methods (PCMs) are widely used to study trait evolution. However, many evolutionary histories involve reticulate evolutionary scenarios, such as hybridization, that violate core assumptions of these methods. In this study, we evaluate how such violations affect the performance of PCMs. In particular, we focus on the ancestral character estimation, evolutionary rate estimation, and model selection. We simulate continuous trait evolution on various phylogenetic network topologies and assess the performance of PCMs that assume a bifurcating tree (i.e., major tree of the network) as the underlying model of evolution. We found that the performance of the tested PCMs was suboptimal. Using random forest, generalized linear models, and model-based clustering, we identified key factors contributing to these inaccuracies. Our results show that frequent and/or recent hybridization accompanied by one ore more transgressive events and rapidly evolving traits (i.e., high evolutionary rate) lead to significant estimation error, especially with respect to rate estimation and model choice. These factors substantially shift trait values away from tree-based model expectations, leading to overall increased error in parameter estimates. Our study demonstrates cases in which PCMs that rely on trees are likely to misinterpret biological histories and offers recommendations for researchers studying systems with complex evolutionary histories.
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