Tootfinder

#PhantastikPrompts 10.06.:
Wie hältst du es mit geschlechtergerechter Sprache?
Wer bei mir mitliest, dürfte gesehen haben, dass ich nach Möglichkeit neutrale Formen verwende (und wo es sich absolut nicht ausgeht damit, den Stern verwende). Bis hin dazu, dass ich sowohl für den Eigengebrauch, als auch für den Fremdgebrauch und die Romanwelt ein paar Neologismen geschaffen ha…

Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study
Leon Mayer, Tim R\"adsch, Dominik Michael, Lucas Luttner, Amine Yamlahi, Evangelia Christodoulou, Patrick Godau, Marcel Knopp, Annika Reinke, Fiona Kolbinger, Lena Maier-Hein
https://arxiv.org/abs/2506.06232

Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study
While traditional computer vision models have historically struggled to generalize to endoscopic domains, the emergence of foundation models has shown promising cross-domain performance. In this work, we present the first large-scale study assessing the capabilities of Vision Language Models (VLMs) for endoscopic tasks with a specific focus on laparoscopic surgery. Using a diverse set of state-of-the-art models, multiple surgical datasets, and extensive human reference annotations, we address t…

DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation
Jingyu Xiao, Ming Wang, Man Ho Lam, Yuxuan Wan, Junliang Liu, Yintong Huo, Michael R. Lyu
https://arxiv.org/abs/2506.06251

DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in automated front-end engineering, e.g., generating UI code from visual designs. However, existing front-end UI code generation benchmarks have the following limitations: (1) While framework-based development becomes predominant in modern front-end programming, current benchmarks fail to incorporate mainstream development frameworks. (2) Existing evaluations focus solely on the UI code generation task, whereas p…

A Survey of Earable Technology: Trends, Tools, and the Road Ahead
Changshuo Hu, Qiang Yang, Yang Liu, Tobias R\"oddiger, Kayla-Jade Butkow, Mathias Ciliberto, Adam Luke Pullin, Jake Stuchbury-Wass, Mahbub Hassan, Cecilia Mascolo, Dong Ma
https://arxiv.org/abs/2506.05720

A Survey of Earable Technology: Trends, Tools, and the Road Ahead
Earable devices, wearables positioned in or around the ear, are undergoing a rapid transformation from audio-centric accessories into multifunctional systems for interaction, contextual awareness, and health monitoring. This evolution is driven by commercial trends emphasizing sensor integration and by a surge of academic interest exploring novel sensing capabilities. Building on the foundation established by earlier surveys, this work presents a timely and comprehensive review of earable resea…

Bivariate asymptotics via random walks: application to large genus maps
Andrew Elvey Price, Wenjie Fang, Baptiste Louf, Michael Wallner
https://arxiv.org/abs/2506.06924

Bivariate asymptotics via random walks: application to large genus maps
We obtain bivariate asymptotics for the number of (unicellular) combinatorial maps (a model of discrete surfaces) as both the size and the genus grow. This work is related to two research topics that have been very active recently: multivariate asymptotics and large genus geometry. Our method consists in studying a linear recurrence for these numbers, and in fact it can be applied to many other linear recurrences. We discuss briefly the generality of our method and future research directions.

Infinite Time Turing Machines and their Applications
Rukmal Weerawarana, Maxwell Braun
https://arxiv.org/abs/2506.05351 https://arxiv…

Infinite Time Turing Machines and their Applications
This work establishes a rigorous theoretical foundation for analyzing deep learning systems by leveraging Infinite Time Turing Machines (ITTMs), which extend classical computation into transfinite ordinal steps. Using ITTMs, we reinterpret modern architectures like Transformers, revealing fundamental limitations in scalability, efficiency, and interpretability. Building on these insights, we propose the Universal State Machine (USM), a novel computational paradigm designed from first principles…

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine
Anushka Jha, Tanushree Dewangan, Mukul Lokhande, Santosh Kumar Vishvakarma
https://arxiv.org/abs/2506.07046

QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine
Reinforcement Learning (RL) has outperformed other counterparts in sequential decision-making and dynamic environment control. However, FPGA deployment is significantly resource-expensive, as associated with large number of computations in training agents with high-quality images and possess new challenges. In this work, we propose QForce-RL takes benefits of quantization to enhance throughput and reduce energy footprint with light-weight RL architecture, without significant performance degrada…

Comprehensive Radio Monitoring of the Black Hole X-ray Binary Swift J1727.8$-$1613 during its 2023$-$2024 Outburst
Andrew K. Hughes, Francesco Carotenuto, Thomas D. Russell, Alexandra J. Tetarenko, James C. A. Miller-Jones, Arash Bahramian, Joe S. Bright, Fraser J. Cowie, Rob Fender, Mark A. Gurwell, Jasvinderjit K. Khaulsay, Anastasia Kirby, Serena Jones, Elodie Lescure, Michael McCollough, Richard M. Plotkin, Ramprasad Rao, Saeqa D. Vrtilek, David R. A. Williams-Baldwin, Callan M. Wo…

Comprehensive Radio Monitoring of the Black Hole X-ray Binary Swift J1727.8$-$1613 during its 2023$-$2024 Outburst
This work presents comprehensive multi-frequency radio monitoring of the black hole low-mass X-ray binary Swift J1727.8$-$1613, which underwent its first recorded outburst after its discovery in August 2023. Through a considerable community effort, we have coalesced the data from multiple, distinct observing programs; the light curves include ${\sim} 10$ months and 197 epochs of monitoring from 7 radio facilities with observing frequencies ranging from (approximately) 0.3$-$230GHz. The primary …

Dense Associative Memory in a Nonlinear Optical Hopfield Neural Network
Khalid Musa, Santosh Kumar, Michael Katidis, Yu-Ping Huang
https://arxiv.org/abs/2506.07849

Dense Associative Memory in a Nonlinear Optical Hopfield Neural Network
Modern Hopfield Neural Networks (HNNs), also known as Dense Associative Memories (DAMs), enhance the performance of simple recurrent neural networks by leveraging the nonlinearities in their energy functions. They have broad applications in combinatorial optimization, high-capacity memory storage, deep learning transformers, and correlated pattern recognition. Thus far, research on DAMs has been primarily theoretical, with implementations limited to CPUs and GPUs. In this work, for the first ti…

Methane leaks from dormant oil and gas wells in Canada are seven times worse than thought, study suggests #Canada

Methane leaks from dormant oil and gas wells in Canada are seven times worse than thought, study suggests
Methane emissions from Canada's non-producing oil and gas wells appear to be seven times higher than government estimates, according to a new study led by researchers at McGill University. The findings spotlight a major gap in the country's official greenhouse gas inventory and raise urgent questions about how methane leaks are monitored, reported and managed.