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Rund 70 Mitglieder der #BürgerEnergie-Genossenschaft 58 trafen sich an der PV-#Freiflächenanlage in #Letmathe, um sich über Technik und Besonderheiten der Anlage zu informiere…

BEG-58 Aktiventreffen an der PV Freiflächenanlage 1 in Letmathe - BEG-58
Aktiventreff an der PV Freiflächenanlage der Bürgerenergiegensossenschaft BEG-58 in Lethmate bei Iserlohn

CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis
Jiayi Wang, Hadrien Reynaud, Franciskus Xaverius Erick, Bernhard Kainz
https://arxiv.org/abs/2508.12900 https://

CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis
Generative modelling of entire CT volumes conditioned on clinical reports has the potential to accelerate research through data augmentation, privacy-preserving synthesis and reducing regulator-constraints on patient data while preserving diagnostic signals. With the recent release of CT-RATE, a large-scale collection of 3D CT volumes paired with their respective clinical reports, training large text-conditioned CT volume generation models has become achievable. In this work, we introduce CTFlo…

Dynamic Latent Class Structural Equation Modeling: A Hands-On Tutorial for Modeling Intensive Longitudinal Data
Roberto Faleh, Sofia Morelli, Vivato Andriamiarana, Zachary J. Roman, Christoph Fl\"uckiger, Holger Brandt
https://arxiv.org/abs/2508.12983

Dynamic Latent Class Structural Equation Modeling: A Hands-On Tutorial for Modeling Intensive Longitudinal Data
In this tutorial, we provide a hands-on guideline on how to implement complex Dynamic Latent Class Structural Equation Models (DLCSEM) in the Bayesian software JAGS. We provide building blocks starting with simple Confirmatory Factor and Time Series analysis, and then extend these blocks to Multilevel Models and Dynamic Structural Equation Models (DSEM). Leading through the tutorial is an example from clinical psychology using data on a generalized anxiety treatment that includes scales on anxi…

MATPAC : Enhanced Masked Latent Prediction for Self-Supervised Audio Representation Learning
Aurian Quelennec, Pierre Chouteau, Geoffroy Peeters, Slim Essid
https://arxiv.org/abs/2508.12709

MATPAC++: Enhanced Masked Latent Prediction for Self-Supervised Audio Representation Learning
Masked latent prediction has emerged as a leading paradigm in self-supervised learning (SSL), especially for general audio and music representation learning. While recent methods have demonstrated strong performance, the role of the predictor module used at the output of such SSL systems remains mainly overlooked, despite being crucial for solving the pretext task at hand. In particular, this module should be able to deal with the ambiguity inherent in audio content, especially when it is compo…

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Niklas Bubeck, Suprosanna Shit, Chen Chen, Can Zhao, Pengfei Guo, Dong Yang, Georg Zitzlsberger, Daguang Xu, Bernhard Kainz, Daniel Rueckert, Jiazhen Pan
https://arxiv.org/abs/2508.13826

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limited by the sparse acquisition of 2D short-axis slices, resulting in incomplete volumetric information. Accurate 3D reconstruction from these sparse slices is essential for comprehensive cardiac assessment, but existing methods face challenges, including reliance on predefined interpolation schemes (e.g., linear or spherical), computational inefficiency, an…

Generative Co-Design of Antibody Sequences and Structures via Black-Box Guidance in a Shared Latent Space
Yinghua Yao, Yuangang Pan, Xixian Chen
https://arxiv.org/abs/2508.11424

Generative Co-Design of Antibody Sequences and Structures via Black-Box Guidance in a Shared Latent Space
Advancements in deep generative models have enabled the joint modeling of antibody sequence and structure, given the antigen-antibody complex as context. However, existing approaches for optimizing complementarity-determining regions (CDRs) to improve developability properties operate in the raw data space, leading to excessively costly evaluations due to the inefficient search process. To address this, we propose LatEnt blAck-box Design (LEAD), a sequence-structure co-design framework that opt…

🎧 Avec Ricardo, explorez 40 minutes d’un récit intense sur OKC : comment une communauté hippie bouddhiste a dérivé, séparant parents et enfants, menant Š des abus. Aujourd’hui, certains de ces enfants luttent pour la justice.
https://chardonsbleus.org/podcast-deri

Coding agents have crossed a chasm
https://blog.singleton.io/posts/2025-06-14-coding-agents-cross-a-chasm/

Privacy-Preserving Chest X-ray Classification in Latent Space with Homomorphically Encrypted Neural Inference
Jonghun Kim, Gyeongdeok Jo, Shinyoung Ra, Hyunjin Park
https://arxiv.org/abs/2506.15258

Privacy-Preserving Chest X-ray Classification in Latent Space with Homomorphically Encrypted Neural Inference
Medical imaging data contain sensitive patient information requiring strong privacy protection. Many analytical setups require data to be sent to a server for inference purposes. Homomorphic encryption (HE) provides a solution by allowing computations to be performed on encrypted data without revealing the original information. However, HE inference is computationally expensive, particularly for large images (e.g., chest X-rays). In this study, we propose an HE inference framework for medical i…

VITA: Vision-to-Action Flow Matching Policy
Dechen Gao, Boqi Zhao, Andrew Lee, Ian Chuang, Hanchu Zhou, Hang Wang, Zhe Zhao, Junshan Zhang, Iman Soltani
https://arxiv.org/abs/2507.13231

VITA: Vision-to-Action Flow Matching Policy
We present VITA, a Vision-To-Action flow matching policy that evolves latent visual representations into latent actions for visuomotor control. Traditional flow matching and diffusion policies sample from standard source distributions (e.g., Gaussian noise) and require additional conditioning mechanisms like cross-attention to condition action generation on visual information, creating time and space overheads. VITA proposes a novel paradigm that treats latent images as the flow source, learnin…