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@mlncn@social.coop
2025-12-19 01:30:28

defend612.com has been blocked at the domain name level for me and at least two other people over T-Mobile networks (which includes GoogleFi, MetroPCS, and… wait for it… Trump Mobile). For at least all day today.
Seems unlikely to be a technical problem but instead to be Actual Censorship (unless its not …

Screenshot of a smartphone browser with defend612.com in the address bar and a sketch of a sad piece of paper followed by the text: This site can't be reached. Check if there is a typo in defend612.com DNS_PROBE_FINISHED_NXDOMAIN
@arXiv_csCV_bot@mastoxiv.page
2025-09-29 11:19:47

B\'ezier Meets Diffusion: Robust Generation Across Domains for Medical Image Segmentation
Chen Li, Meilong Xu, Xiaoling Hu, Weimin Lyu, Chao Chen
arxiv.org/abs/2509.22476

Bézier Meets Diffusion: Robust Generation Across Domains for Medical Image Segmentation
Training robust learning algorithms across different medical imaging modalities is challenging due to the large domain gap. Unsupervised domain adaptation (UDA) mitigates this problem by using annotated images from the source domain and unlabeled images from the target domain to train the deep models. Existing approaches often rely on GAN-based style transfer, but these methods struggle to capture cross-domain mappings in regions with high variability. In this paper, we propose a unified framew…

@arXiv_csAI_bot@mastoxiv.page
2025-09-29 10:29:27

InfiMed-Foundation: Pioneering Advanced Multimodal Medical Models with Compute-Efficient Pre-Training and Multi-Stage Fine-Tuning
Guanghao Zhu, Zhitian Hou, Zeyu Liu, Zhijie Sang, Congkai Xie, Hongxia Yang
arxiv.org/abs/2509.22261

InfiMed-Foundation: Pioneering Advanced Multimodal Medical Models with Compute-Efficient Pre-Training and Multi-Stage Fine-Tuning
Multimodal large language models (MLLMs) have shown remarkable potential in various domains, yet their application in the medical field is hindered by several challenges. General-purpose MLLMs often lack the specialized knowledge required for medical tasks, leading to uncertain or hallucinatory responses. Knowledge distillation from advanced models struggles to capture domain-specific expertise in radiology and pharmacology. Additionally, the computational cost of continual pretraining with lar…

@arXiv_csCR_bot@mastoxiv.page
2025-10-09 09:28:51

Code Agent can be an End-to-end System Hacker: Benchmarking Real-world Threats of Computer-use Agent
Weidi Luo, Qiming Zhang, Tianyu Lu, Xiaogeng Liu, Bin Hu, Hung-Chun Chiu, Siyuan Ma, Yizhe Zhang, Xusheng Xiao, Yinzhi Cao, Zhen Xiang, Chaowei Xiao
arxiv.org/abs/2510.06607

Code Agent can be an End-to-end System Hacker: Benchmarking Real-world Threats of Computer-use Agent
Computer-use agent (CUA) frameworks, powered by large language models (LLMs) or multimodal LLMs (MLLMs), are rapidly maturing as assistants that can perceive context, reason, and act directly within software environments. Among their most critical applications is operating system (OS) control. As CUAs in the OS domain become increasingly embedded in daily operations, it is imperative to examine their real-world security implications, specifically whether CUAs can be misused to perform realistic…

@arXiv_csCL_bot@mastoxiv.page
2025-10-06 10:12:49

Self-Improvement in Multimodal Large Language Models: A Survey
Shijian Deng, Kai Wang, Tianyu Yang, Harsh Singh, Yapeng Tian
arxiv.org/abs/2510.02665

Self-Improvement in Multimodal Large Language Models: A Survey
Recent advancements in self-improvement for Large Language Models (LLMs) have efficiently enhanced model capabilities without significantly increasing costs, particularly in terms of human effort. While this area is still relatively young, its extension to the multimodal domain holds immense potential for leveraging diverse data sources and developing more general self-improving models. This survey is the first to provide a comprehensive overview of self-improvement in Multimodal LLMs (MLLMs). …

@buercher@tooting.ch
2025-10-18 09:00:36

Vaud subventionne l’église protestante avec 64 millions par a pour ses services Š tous. Vu que l’église diminue ses services avec la suppression de protestinfo, Vaud devrait ajuster sa subvention.

@arXiv_csCV_bot@mastoxiv.page
2025-10-06 10:17:39

LEAML: Label-Efficient Adaptation to Out-of-Distribution Visual Tasks for Multimodal Large Language Models
Ci-Siang Lin, Min-Hung Chen, Yu-Yang Sheng, Yu-Chiang Frank Wang
arxiv.org/abs/2510.03232

LEAML: Label-Efficient Adaptation to Out-of-Distribution Visual Tasks for Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) have achieved strong performance on general visual benchmarks but struggle with out-of-distribution (OOD) tasks in specialized domains such as medical imaging, where labeled data is limited and expensive. We introduce LEAML, a label-efficient adaptation framework that leverages both scarce labeled VQA samples and abundant unlabeled images. Our approach generates domain-relevant pseudo question-answer pairs for unlabeled data using a QA generator regulari…

@buercher@tooting.ch
2025-11-29 16:48:59

“on a besoin d’un candidat qui dise au Conseil d’Etat qu’il fait fausse route. C’est un mensonge de répéter qu’on peut baisser les impôts tout en maintenant les prestations. Et Christelle Luisier se trompe en disant que le canton n’a pas de problème de recettes: on a besoin d’une réponse quant aux millions perdus du bouclier fiscal et on a besoin d’un système de solidarité financière”
letemps.ch/suisse/vaud/vaud-ca