Tootfinder

To the genocide-loving German from mastodon.social who reported @… for being antisemitic:
Fuck you, you genocide-loving piece of shit, opposing genocide isn’t antisemitism. What you’re doing – conflating being anti-genocide with being antisemitic – is the greatest antisemitism as you’re de facto stating that all Jews are pro-genocide.

Does anyone have an IEEE DataPort subscription through your work or institution and would be open to sharing some files?
I'd love to get https://ieee-dataport.org/documents/cloud-telescope-internet-background-radiation-may-2023…

So last night, after watching the Russian Victory Day ‘24 video for contrast, I thought “what the hell” & went ahead & watched1️⃣ the PBS YT video of the “June 24 Military2️⃣ Parade” to see if it was as bad as all that
(Short Answer: Yes and No)
But this guy was the one that caught my sympathy as “you poor dude”.

Mein #Passwort ist wohl zu gefährlich... 🙃
> A potentially dangerous Request.Form value was detected from the client (Password="...L~FJj&YNG]<z. e?8dRF").
#Security #AspDotNet

User Perceptions and Attitudes Toward Untraceability in Messaging Platforms
Carla F. Griggio, Boel Nelson, Zefan Sramek, Aslan Askarov
https://arxiv.org/abs/2506.11212

User Perceptions and Attitudes Toward Untraceability in Messaging Platforms
Mainstream messaging platforms offer a variety of features designed to enhance user privacy, such as disappearing messages, password-protected chats, and end-to-end encryption (E2EE), which primarily protect message contents. Beyond contents, the transmission of messages generates metadata that can reveal who communicates with whom, when and how often. In this paper, we study user perceptions of "untraceability", i.e., preventing third parties from tracing who communicates with whom, with the g…

Lyapunov analysis for FISTA under strong convexity
Luis M. Brice\~no-Arias
https://arxiv.org/abs/2506.11785 https://arxiv.org/pdf/250…

Lyapunov analysis for FISTA under strong convexity
In this paper, we conduct a theoretical and numerical study of the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) under strong convexity assumptions. We propose an autonomous Lyapunov function that reflects the strong convexity of the objective function, whether it arises from the smooth or non-smooth component. This Lyapunov function decreases monotonically at a linear rate along the iterations of the algorithm for a fixed inertial parameter. Our analysis demonstrates that the best th…

life comes at you fast

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
Zuzanna Skorniewska, Bartlomiej W. Papiez
https://arxiv.org/abs/2506.11753

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
The adoption of neural network models in medical imaging has been constrained by strict privacy regulations, limited data availability, high acquisition costs, and demographic biases. Deep generative models offer a promising solution by generating synthetic data that bypasses privacy concerns and addresses fairness by producing samples for under-represented groups. However, unlike natural images, medical imaging requires validation not only for fidelity (e.g., Fréchet Inception Score) but also…

Leveraging Reference Documents for Zero-Shot Ranking via Large Language Models
Jieran Li, Xiuyuan Hu, Yang Zhao, Shengyao Zhuang, Hao Zhang
https://arxiv.org/abs/2506.11452

Leveraging Reference Documents for Zero-Shot Ranking via Large Language Models
Large Language Models (LLMs) have demonstrated exceptional performance in the task of text ranking for information retrieval. While Pointwise ranking approaches offer computational efficiency by scoring documents independently, they often yield biased relevance estimates due to the lack of inter-document comparisons. In contrast, Pairwise methods improve ranking accuracy by explicitly comparing document pairs, but suffer from substantial computational overhead with quadratic complexity ($O(n^2)…

Interference in Spectrum-Sharing Integrated Terrestrial and Satellite Networks: Modeling, Approximation, and Robust Transmit Beamforming
Wenjing Cao, Yafei Wang, Tianxiang Ji, Tianyang Cao, Wenjin Wang, Symeon Chatzinotas, Bj\"orn Ottersten
https://arxiv.org/abs/2506.11851

Interference in Spectrum-Sharing Integrated Terrestrial and Satellite Networks: Modeling, Approximation, and Robust Transmit Beamforming
This paper investigates robust transmit (TX) beamforming from the satellite to user terminals (UTs), based on statistical channel state information (CSI). The proposed design specifically targets the mitigation of satellite-to-terrestrial interference in spectrum-sharing integrated terrestrial and satellite networks. By leveraging the distribution information of terrestrial UTs, we first establish an interference model from the satellite to terrestrial systems without shared CSI. Based on this,…