Tootfinder

Berlin-based Payrails, whose platform helps enterprises accept payments, issue payouts, and more, raised a $32M Series A, taking its total funding to $52.8M (Tamara Djurickovic/Tech.eu)
https://tech.eu/2025/06/12/payrails-ra

Payrails raises $32M Series A to boost payment performance for global enterprises
A global payment software company Payrails, has raised $32 million in Series A funding to further its mission of enabling enterprises to take control of their payment operations.  Founded by former fintech leaders from Delivery Hero, Payrails’ founding team brings deep experience building global, scalable financial infrastructure. With the continued backing of leading technology investors, Payrails is strengthening its position as a category-defining payment platform for large enterprises. …

#Meta hat mit „localhost tracking“ ein technisches #Schlupfloch genutzt, das selbst im Inkognito-Modus, mit #VPN und gelöschten

“Localhost tracking” explained. It could cost Meta 32 billion.
You just can't finish off Zuckerberg.

Sigh.
I don't even know what we're fucking doing anymore.
Also, the marketer who passed up the opportunity to call these Brawndrops should be dismissed immediately.

Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Rei Tamaru, Pei Li, Bin Ran
https://arxiv.org/abs/2507.08743 https…

Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Digital Twins (DT) have the potential to transform traffic management and operations by creating dynamic, virtual representations of transportation systems that sense conditions, analyze operations, and support decision-making. A key component for DT of the transportation system is dynamic roadway geometry sensing. However, existing approaches often rely on static maps or costly sensors, limiting scalability and adaptability. Additionally, large-scale DTs that collect and analyze data from mult…

Enhancing Decoding Performance using Efficient Error Learning
Pavithran Iyer, Aditya Jain, Stephen D. Bartlett, Joseph Emerson
https://arxiv.org/abs/2507.08536

Enhancing Decoding Performance using Efficient Error Learning
Lowering the resource overhead needed to achieve fault-tolerant quantum computation is crucial to building scalable quantum computers. We show that adapting conventional maximum likelihood (ML) decoders to a small subset of efficiently learnable physical error characteristics can significantly improve the logical performance of a quantum error-correcting code. Specifically, we leverage error information obtained from efficient characterization methods based on Cycle Error Reconstruction (CER), …

"Programming SCI Clusters Using Parallel CORBA Objects"
That's the title of one of the papers in the book "SCI: Scalable Coherent Interface" and pretty much everything in this title is vintage. #vintagecomputing #hpc

Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning
Chan Young Park, Jillian Fisher, Marius Memmel, Dipika Khullar, Andy Yun, Abhishek Gupta, Yejin Choi
https://arxiv.org/abs/2507.08224

Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning
Large language models (LLMs) have shown promise in robotic procedural planning, yet their human-centric reasoning often omits the low-level, grounded details needed for robotic execution. Vision-language models (VLMs) offer a path toward more perceptually grounded plans, but current methods either rely on expensive, large-scale models or are constrained to narrow simulation settings. We introduce SelfReVision, a lightweight and scalable self-improvement framework for vision-language procedural …

Criteria-Based LLM Relevance Judgments
Naghmeh Farzi, Laura Dietz
https://arxiv.org/abs/2507.09488 https://arxiv.org/pdf/2507.09488…

Criteria-Based LLM Relevance Judgments
Relevance judgments are crucial for evaluating information retrieval systems, but traditional human-annotated labels are time-consuming and expensive. As a result, many researchers turn to automatic alternatives to accelerate method development. Among these, Large Language Models (LLMs) provide a scalable solution by generating relevance labels directly through prompting. However, prompting an LLM for a relevance label without constraints often results in not only incorrect predictions but also…

Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models
Ulzee An, Moonseong Jeong, Simon A. Lee, Aditya Gorla, Yuzhe Yang, Sriram Sankararaman
https://arxiv.org/abs/2507.08254

Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models
Current challenges in developing foundational models for volumetric imaging data, such as magnetic resonance imaging (MRI), stem from the computational complexity of training state-of-the-art architectures in high dimensions and curating sufficiently large datasets of volumes. To address these challenges, we introduce Raptor (Random Planar Tensor Reduction), a train-free method for generating semantically rich embeddings for volumetric data. Raptor leverages a frozen 2D foundation model, pretra…

On the application of large deviation estimates to local solubility in families of varieties
Sun Woo Park, Efthymios Sofos
https://arxiv.org/abs/2507.08173

On the application of large deviation estimates to local solubility in families of varieties
We apply the Gärtner--Ellis theorem on large deviations to prove a weak version of the Loughran--Smeets conjecture for general fibrations.