Tootfinder

Die ePetition 179446 an den Bundestag aus dem Bereich Identität im Internet mit dem Titel
Verbot von Deepfakes von echten Menschen
kann bis zum Mittwoch 23. Juli 2025 01:00 UTC mitgezeichnet werden und lautet
Mit der Petition wird gefordert, Deepfakes von echten Menschen zu verbieten.
und wird begründet…

"Apple’s new AI feature can identify what’s on your #iPhone screen"
Also, #Apple’s new AI Feature…

"Applications of the Critical Incident Technique in Library and Information Science Research: A Literature Review" https://doi.org/10.1515/libri-2024-0065

immigrant rights is a bike issue https://usa.streetsblog.org/2025/05/02/freedom-to-move-why-immigration-belongs-in-the-mobility-conversation

Freedom to Move: Why Immigration Belongs in the Mobility Conversation — Streetsblog USA
"If movement is a right, then that right must apply to everyone regardless of identity."

This https://arxiv.org/abs/2506.01790 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

$IF-GUIDE$: Influence Function-Guided Detoxification of LLMs
We study how training data contributes to the emergence of toxic behaviors in large-language models. Most prior work on reducing model toxicity adopts $reactive$ approaches, such as fine-tuning pre-trained (and potentially toxic) models to align them with human values. In contrast, we propose a $proactive$ approach$-$IF-Guide$-$which leverages influence functions to identify harmful tokens within any training data and suppress their impact during training. To this end, we first show that standa…

This https://arxiv.org/abs/2505.21036 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

RainFusion: Adaptive Video Generation Acceleration via Multi-Dimensional Visual Redundancy
Video generation using diffusion models is highly computationally intensive, with 3D attention in Diffusion Transformer (DiT) models accounting for over 80\% of the total computational resources. In this work, we introduce {\bf RainFusion}, a novel training-free sparse attention method that exploits inherent sparsity nature in visual data to accelerate attention computation while preserving video quality. Specifically, we identify three unique sparse patterns in video generation attention calcu…

This https://arxiv.org/abs/2506.05850 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models
We identify \textbf{Cross-lingual Collapse}, a systematic drift in which the chain-of-thought (CoT) of a multilingual language model reverts to its dominant pre-training language even when the prompt is expressed in a different language. Recent large language models (LLMs) with reinforcement learning with verifiable reward (RLVR) have achieved strong logical reasoning performances by exposing their intermediate reasoning traces, giving rise to large reasoning models (LRMs). However, the mechani…

This https://arxiv.org/abs/2506.03589 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

BiMa: Towards Biases Mitigation for Text-Video Retrieval via Scene Element Guidance
Text-video retrieval (TVR) systems often suffer from visual-linguistic biases present in datasets, which cause pre-trained vision-language models to overlook key details. To address this, we propose BiMa, a novel framework designed to mitigate biases in both visual and textual representations. Our approach begins by generating scene elements that characterize each video by identifying relevant entities/objects and activities. For visual debiasing, we integrate these scene elements into the vide…

This https://arxiv.org/abs/2506.00436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
In this study, we propose a method for identifying potential customers in targeted marketing by applying learning from positive and unlabeled data (PU learning). We consider a scenario in which a company sells a product and can observe only the customers who purchased it. Decision-makers seek to market products effectively based on whether people have loyalty to the company. Individuals with loyalty are those who are likely to remain interested in the company even without additional advertising…

This https://arxiv.org/abs/2505.19038 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias
Accurately predicting the long-term evolution of turbulence is crucial for advancing scientific understanding and optimizing engineering applications. However, existing deep learning methods face significant bottlenecks in long-term autoregressive prediction, which exhibit excessive smoothing and fail to accurately track complex fluid dynamics. Our extensive experimental and spectral analysis of prevailing methods provides an interpretable explanation for this shortcoming, identifying Spectral …