Tootfinder

Scientists Capture Sharpest Images Ever of Sun's Corona | PetaPixel https://petapixel.com/2025/05/30/scientists-capture-sharpest-images-ever-of-suns-corona/
There's video in there you might want to full-screen.

Scientists Capture Sharpest Images Ever of Sun's Corona
You've never seen the Sun like this.

The iNaturalist Sounds Dataset
Mustafa Chasmai, Alexander Shepard, Subhransu Maji, Grant Van Horn
https://arxiv.org/abs/2506.00343 https://

The iNaturalist Sounds Dataset
We present the iNaturalist Sounds Dataset (iNatSounds), a collection of 230,000 audio files capturing sounds from over 5,500 species, contributed by more than 27,000 recordists worldwide. The dataset encompasses sounds from birds, mammals, insects, reptiles, and amphibians, with audio and species labels derived from observations submitted to iNaturalist, a global citizen science platform. Each recording in the dataset varies in length and includes a single species annotation. We benchmark multi…

Substructural Abstract Syntax with Variable Binding and Single-Variable Substitution
Marcelo Fiore, Sanjiv Ranchod
https://arxiv.org/abs/2505.24812 https:/…

Substructural Abstract Syntax with Variable Binding and Single-Variable Substitution
We develop a unified categorical theory of substructural abstract syntax with variable binding and single-variable (capture-avoiding) substitution. This is done for the gamut of context structural rules given by exchange (linear theory) with weakening (affine theory) or with contraction (relevant theory) and with both (cartesian theory). Specifically, in all four scenarios, we uniformly: define abstract syntax with variable binding as free algebras for binding-signature endofunctors over variab…

Towards Source Attribution of Singing Voice Deepfake with Multimodal Foundation Models
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar, Swarup Ranjan Behera, Priyabrata Mallick, Pailla Balakrishna Reddy, Arun Balaji Buduru, Rajesh Sharma
https://arxiv.org/abs/2506.03364

Towards Source Attribution of Singing Voice Deepfake with Multimodal Foundation Models
In this work, we introduce the task of singing voice deepfake source attribution (SVDSA). We hypothesize that multimodal foundation models (MMFMs) such as ImageBind, LanguageBind will be most effective for SVDSA as they are better equipped for capturing subtle source-specific characteristics-such as unique timbre, pitch manipulation, or synthesis artifacts of each singing voice deepfake source due to their cross-modality pre-training. Our experiments with MMFMs, speech foundation models and mus…

Fast synthesis of turbulence with multi-scale coherent vortices
Zishuo Han, Weiyu Shen, Yue Yang
https://arxiv.org/abs/2506.01022 https://

Fast synthesis of turbulence with multi-scale coherent vortices
Turbulence synthesis methods often struggle to reproduce coherent vortices while capturing key statistical features. We introduce a fast synthetic turbulence method to generate instantaneous turbulent fields (termed `woven turbulence') with multi-scale vortices, combining the advantages of fractal models and coherent structures. The method generates multi-scale Burgers vortex tubes based on stochastic centerlines constructed by the fractional Brownian bridge. The largest and smallest vortices g…

TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
Shengyuan Liu, Wenting Chen, Boyun Zheng, Wentao Pan, Xiang Li, Yixuan Yuan
https://arxiv.org/abs/2505.24687

TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
Tumor data synthesis offers a promising solution to the shortage of annotated medical datasets. However, current approaches either limit tumor diversity by using predefined masks or employ computationally expensive two-stage processes with multiple denoising steps, causing computational inefficiency. Additionally, these methods typically rely on binary masks that fail to capture the gradual transitions characteristic of tumor boundaries. We present TumorGen, a novel Boundary-Aware Tumor-Mask Sy…

Finished the SFP28 carrier board. Still waiting on final design review before I send it out to OSHPark, but it's done-enough that I could export a STEP and pull into the enclosure model.
Placement of all the front panel stuff is pretty definite at this point, the power supply and PDU are likely to move.
Logic board hasn't even started schematic capture yet.
There will probably be at least a couple of fans but I haven't figured out where or how many yet.

A High-Performance Evolutionary Multiobjective Community Detection Algorithm
Guilherme O. Santos, Lucas S. Vieira, Giulio Rossetti, Carlos H. G. Ferreira, Gladston Moreira
https://arxiv.org/abs/2506.01752

A High-Performance Evolutionary Multiobjective Community Detection Algorithm
Community structure is a key feature of complex networks, underpinning a diverse range of phenomena across social, biological, and technological systems. While traditional methods, such as Louvain and Leiden, offer efficient solutions, they rely on single-objective optimization, often failing to capture the multifaceted nature of real-world networks. Multi-objective approaches address this limitation by considering multiple structural criteria simultaneously, but their high computational cost r…

This https://arxiv.org/abs/2504.08756 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation
Existing RAG benchmarks often overlook query difficulty, leading to inflated performance on simpler questions and unreliable evaluations. A robust benchmark dataset must satisfy three key criteria: quality, diversity, and difficulty, which capturing the complexity of reasoning based on hops and the distribution of supporting evidence. In this paper, we propose MHTS (Multi-Hop Tree Structure), a novel dataset synthesis framework that systematically controls multi-hop reasoning complexity by leve…

Are Mamba-based Audio Foundation Models the Best Fit for Non-Verbal Emotion Recognition?
Mohd Mujtaba Akhtar, Orchid Chetia Phukan, Girish, Swarup Ranjan Behera, Ananda Chandra Nayak, Sanjib Kumar Nayak, Arun Balaji Buduru, Rajesh Sharma
https://arxiv.org/abs/2506.02258

Are Mamba-based Audio Foundation Models the Best Fit for Non-Verbal Emotion Recognition?
In this work, we focus on non-verbal vocal sounds emotion recognition (NVER). We investigate mamba-based audio foundation models (MAFMs) for the first time for NVER and hypothesize that MAFMs will outperform attention-based audio foundation models (AAFMs) for NVER by leveraging its state-space modeling to capture intrinsic emotional structures more effectively. Unlike AAFMs, which may amplify irrelevant patterns due to their attention mechanisms, MAFMs will extract more stable and context-aware…