Tootfinder

Good stuff for Mac Users. I’ve used it forever. Really handy, and it works well.
#Software #Mac #MacOS

St. Clair Software (@stclairsoft@mstdn.social)
Attached: 1 image Default Folder X 6.1.6 is out! It includes a Focus filter to automatically switch folder sets when you activate a macOS Focus. It's also faster in file dialogs and fixes issues with keyboard shortcuts, Finder-click, keyboard focus and making new folders. https://www.stclairsoft.com/blog/2025/05/13/default-folder-x-6-1-6-automatically-swap-favorites-and-default-folders-when-you-switch-macos-focus-modes

If you're annoyed by macos hiding the draggable file icons a few versions ago, you can force it to always show them:
https://apple.stackexchange.com/questions/413667/how-can-i-make-the-draggable…

"The places that need to worry are those that look vulnerable or unlikely to fight back. Greenland may fall into that category — which suggests that Denmark and the EU need to find ways of letting Trump know that there will be a price to pay if he makes a move on the island"
Trump always chickens out on foreign policy too - https://on.ft.com/458Z5FI v

Ayo, since when did #Spotify (free) start placing ads over cover art???
(Sidenote: Since switching to a self-hosted music streaming server, I only use Spotify to bookmark new songs, so I'm not on there much. This is another good reason to ditch streaming services)

This https://arxiv.org/abs/2410.11470 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Almost Optimal Fully Dynamic $k$-Center Clustering with Recourse
In this paper, we consider the \emph{metric $k$-center} problem in the fully dynamic setting, where we are given a metric space $(V,d)$ evolving via a sequence of point insertions and deletions and our task is to maintain a subset $S \subseteq V$ of at most $k$ points that minimizes the objective $\max_{x \in V} \min_{y \in S}d(x, y)$. We want to design our algorithm so that we minimize its \emph{approximation ratio}, \emph{recourse} (the number of changes it makes to the solution $S$), and \em…

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Sayyed Farid Ahamed, Sandip Roy, Soumya Banerjee, Marc Vucovich, Kevin Choi, Abdul Rahman, Alison Hu, Edward Bowen, Sachin Shetty
https://arxiv.org/abs/2505.23791

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Federated Learning (FL) is a collaborative learning framework designed to protect client data, yet it remains highly vulnerable to Intellectual Property (IP) threats. Model extraction (ME) attacks pose a significant risk to Machine Learning as a Service (MLaaS) platforms, enabling attackers to replicate confidential models by querying black-box (without internal insight) APIs. Despite FL's privacy-preserving goals, its distributed nature makes it particularly susceptible to such attacks. This p…

I maintain #macos #osx #macosx

#NowPlaying - really great live album by horse lords, 2018-2023. music to make you feel like you're spinning around fast & getting giddy, but without leaving the couch (plus some space zones). https://

$\lambda$ and $\rho$ Regge trajectories for the pentaquark $P_{cc\bar{c}bb}$ in the diquark-triquark picture
He Song, Xin-Ru Liu, Jia-Qi Xie, Jiao-Kai Chen
https://arxiv.org/abs/2506.01005

$λ$ and $ρ$ Regge trajectories for the pentaquark $P_{cc\bar{c}bb}$ in the diquark-triquark picture
We propose the Regge trajectory relations for the fully heavy pentaquark $P_{cc\bar{c}bb}$ utilizing both diquark and triquark Regge trajectory relations. Using these new relations, we discuss four series of Regge trajectories: the $ρ_1$-, $ρ_2$-, $λ_1$-, and $λ_2$-trajectories. We provide rough estimates for the masses of the $ρ_1$-, $ρ_2$-, $λ_1$-, and $λ_2$-excited states. Except for the $λ_1$-trajectories, the complete forms of the other three series of Regge trajectories for the p…

RPRA-ADD: Forgery Trace Enhancement-Driven Audio Deepfake Detection
Ruibo Fu, Xiaopeng Wang, Zhengqi Wen, Jianhua Tao, Yuankun Xie, Zhiyong Wang, Chunyu Qiang, Xuefei Liu, Cunhang Fan, Chenxing Li, Guanjun Li
https://arxiv.org/abs/2506.00375

RPRA-ADD: Forgery Trace Enhancement-Driven Audio Deepfake Detection
Existing methods for deepfake audio detection have demonstrated some effectiveness. However, they still face challenges in generalizing to new forgery techniques and evolving attack patterns. This limitation mainly arises because the models rely heavily on the distribution of the training data and fail to learn a decision boundary that captures the essential characteristics of forgeries. Additionally, relying solely on a classification loss makes it difficult to capture the intrinsic difference…