Tootfinder

Aligned Hierarchical Black Hole Mergers in AGN disks revealed by GWTC-4
Yin-Jie Li, Yuan-Zhu Wang, Shao-Peng Tang, Yi-Zhong Fan
https://arxiv.org/abs/2509.23897 https://

Aligned Hierarchical Black Hole Mergers in AGN disks revealed by GWTC-4
The active galactic nucleus (AGN) accretion disks are ideal sites for hierarchical black hole (BH) mergers. To robustly probe such a possibility, we analyze binary black hole mergers in the GWTC-4 with a flexible mixture population model for component masses, spin magnitudes, and spin tilt angles, and identify two distinct subpopulations. In the second subpopulation characterized by high spin magnitudes $χ\sim 0.8$ as well as the broad mass distribution up to $\gtrsim 150M_\odot$, we find a pr…

I've probably mentioned that I'm working on switching #Gentoo from our half-broken eselect-ldso logic to #FlexiBLAS. This also involves a transition period where both setups would be supported.
A good thing is that the switch is ABI-compatible with the previous state (or at least it's supposed to be — we're working with upstream on fixing function coverage). Since libblas.so, liblapack.so and the rest are replaced by symlinks, programs that link to them will simply start using FlexiBLAS. So far, so good.
Unfortunately, switching the other way doesn't work as well. Stuff newly built against our libblas.so & co. symlinks naturally reads FlexiBLAS's SONAME from them, and links to libflexiblas directly. So should you decide to switch back, some packages will stay linked to FlexiBLAS and will need to rebuilt.
In order to avoid this, I would have to replace the symlinks with wrapper libraries, having libblas.so.3 and so on SONAMEs, and linking to libflexiblas. Unfortunately, a dummy wrapper isn't going to work — the linker will complain about using indirect symbols from libflexiblas.so. So I would probably have to "reexport" their symbols somehow, and ideally split into appropriate libraries, so that `-Wl,--as-needed` wouldn't drop some of them. But how to do that?
Well, let's look at the existing logic for eselect-ldso — clearly both BLIS and OpenBLAS create some wrappers. So I've spent some time investigating upstream Makefiles, and literally couldn't find the respective targets. I mean, these are quite complex Makefiles, but I'm grepping hard and can't find even a partial match.
As it turns out, these Makefile targets are added by Gentoo-specific patches. And these patches are just horrible. In case of OpenBLAS, they create the wrapper libraries by linking all the relevant .o files from OpenBLAS build, plus the shared OpenBLAS library. So the OpenBLAS symbols relevant to each interface end up duplicated in libblas.so, liblapack.so, etc., and apparently the symbols needed by them are taken from libopenblas.so. The individual interface libraries aren't even linked to one another, so they expose their own duplicate symbols, but use the implementation from OpenBLAS instead.
BLIS is even worse — the patch is simply creating libblas.so and libcblas.so, using all BLIS objects directly, plus symbol visibility to hide symbols irrelevant to the library. So yes, libblis.so, libblas.so and libcblas.so are roughly three separate copies of the same library, differing only in symbol visibility. And of course libcblas.so doesn't use libblas.so.
Truly #GSoC quality.

"Omni’s take at integrating Apple’s foundation models is the most elegant, complete, and streamlined I’ve seen in a while. Their integration into Apple’s Foundation Models framework is as comprehensive and flexible as you’d expect from them, and I can’t recommend this update enough." — Marcus Mendes
https://

OmniFocus 4.8 gets on-device Apple Intelligence support - 9to5Mac
You can now tap into Apple’s local model for a more robust Omni Automation feature set, starting with 7 plug-ins. Here are the details.

As Microsoft EOLs Windows 10, I'm reminded of why Windows is fundamentally broken, by design. https://davelane.nz/windows-broken-design When you're a global platform monopoly, your existing users are your biggest competition. Your interests and those of your customers are diametricall…

Video-to-BT: Generating Reactive Behavior Trees from Human Demonstration Videos for Robotic Assembly
Xiwei Zhao, Yiwei Wang, Yansong Wu, Fan Wu, Teng Sun, Zhonghua Miao, Sami Haddadin, Alois Knoll
https://arxiv.org/abs/2509.16611

Video-to-BT: Generating Reactive Behavior Trees from Human Demonstration Videos for Robotic Assembly
Modern manufacturing demands robotic assembly systems with enhanced flexibility and reliability. However, traditional approaches often rely on programming tailored to each product by experts for fixed settings, which are inherently inflexible to product changes and lack the robustness to handle variations. As Behavior Trees (BTs) are increasingly used in robotics for their modularity and reactivity, we propose a novel hierarchical framework, Video-to-BT, that seamlessly integrates high-level co…

Multistage Robust Optimization for Time-Decoupled Power Flexibility Aggregation with Energy Storage
Rui Xie, Yue Chen
https://arxiv.org/abs/2508.14477 https://

Multistage Robust Optimization for Time-Decoupled Power Flexibility Aggregation with Energy Storage
To mitigate global climate change, distributed energy resources (DERs), such as distributed generators, flexible loads, and energy storage systems (ESSs), have witnessed rapid growth in power distribution systems. When properly managed, these DERs can provide significant flexibility to power systems, enhancing both reliability and economic efficiency. Due to their relatively small scale, DERs are typically managed by the distribution system operator (DSO), who interacts with the transmission sy…

SSNet: Flexible and robust channel extrapolation for fluid antenna systems enabled by an self-supervised learning framework
Yuan Gao, Yiming Liu, Runze Yu, Shengli Liu, Yanliang Jin, Shunqing Zhang, Shugong Xu, Xiaoli Chu
https://arxiv.org/abs/2509.17797

SSNet: Flexible and robust channel extrapolation for fluid antenna systems enabled by an self-supervised learning framework
Fluid antenna systems (FAS) signify a pivotal advancement in 6G communication by enhancing spectral efficiency and robustness. However, obtaining accurate channel state information (CSI) in FAS poses challenges due to its complex physical structure. Traditional methods, such as pilot-based interpolation and compressive sensing, are not only computationally intensive but also lack adaptability. Current extrapolation techniques relying on rigid parametric models do not accommodate the dynamic env…

Real-Time, Population-Based Reconstruction of 3D Bone Models via Very-Low-Dose Protocols
Yiqun Lin, Haoran Sun, Yongqing Li, Rabia Aslam, Lung Fung Tse, Tiange Cheng, Chun Sing Chui, Wing Fung Yau, Victorine R. Le Meur, Meruyert Amangeldy, Kiho Cho, Yinyu Ye, James Zou, Wei Zhao, Xiaomeng Li
https://arxiv.org/abs/2508.13947

Real-Time, Population-Based Reconstruction of 3D Bone Models via Very-Low-Dose Protocols
Patient-specific bone models are essential for designing surgical guides and preoperative planning, as they enable the visualization of intricate anatomical structures. However, traditional CT-based approaches for creating bone models are limited to preoperative use due to the low flexibility and high radiation exposure of CT and time-consuming manual delineation. Here, we introduce Semi-Supervised Reconstruction with Knowledge Distillation (SSR-KD), a fast and accurate AI framework to reconstr…

Unity based virtual reality for detector and event visualization in JUNO experiment
Kai-Xuan Huang, Tian-Zi Song, Yu-Ning Su, Cheng-Xin Wu, Xue-Sen Wang, Yu-Mei Zhang, Zheng-Yun You
https://arxiv.org/abs/2509.16285

Unity based virtual reality for detector and event visualization in JUNO experiment
Detector and event visualization are crucial components of high-energy physics~(HEP) experimental software. Virtual Reality~(VR) technologies and multimedia development platforms such as Unity offer enhanced display effects and flexible extensibility for visualization in HEP experiments. In this study, we present a VR-based method for detector and event displays in the Jiangmen Underground Neutrino Observatory~(JUNO) experiment. This method shares the same detector geometry descriptions and eve…

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
Ya\c{s}ar Utku Al\c{c}alar, Junno Yun, Mehmet Ak\c{c}akaya
https://arxiv.org/abs/2509.09880

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
Diffusion/score-based models have recently emerged as powerful generative priors for solving inverse problems, including accelerated MRI reconstruction. While their flexibility allows decoupling the measurement model from the learned prior, their performance heavily depends on carefully tuned data fidelity weights, especially under fast sampling schedules with few denoising steps. Existing approaches often rely on heuristics or fixed weights, which fail to generalize across varying measurement …